Triple
T95413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HTML |
E1918
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object |
SGML
SGML (Standard Generalized Markup Language) is a standardized metalanguage for defining markup languages used to structure and describe the content of electronic documents.
|
E8666
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: SGML | Statement: [HTML, basedOn, SGML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SGML Context triple: [HTML, basedOn, SGML]
-
A.
XML
XML (Extensible Markup Language) is a flexible, text-based markup language designed for structuring, storing, and transporting data in a platform-independent way.
-
B.
SVG
SVG (Scalable Vector Graphics) is an XML-based vector image format for two-dimensional graphics that supports interactivity and animation, widely used for web graphics due to its scalability and resolution independence.
-
C.
HTML
HTML (HyperText Markup Language) is the standard markup language used to structure and present content on the World Wide Web.
-
D.
SAS
SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
-
E.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: SGML Triple: [HTML, basedOn, SGML]
Generated description
SGML (Standard Generalized Markup Language) is a standardized metalanguage for defining markup languages used to structure and describe the content of electronic documents.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SGML Target entity description: SGML (Standard Generalized Markup Language) is a standardized metalanguage for defining markup languages used to structure and describe the content of electronic documents.
-
A.
XML
XML (Extensible Markup Language) is a flexible, text-based markup language designed for structuring, storing, and transporting data in a platform-independent way.
-
B.
SVG
SVG (Scalable Vector Graphics) is an XML-based vector image format for two-dimensional graphics that supports interactivity and animation, widely used for web graphics due to its scalability and resolution independence.
-
C.
HTML
HTML (HyperText Markup Language) is the standard markup language used to structure and present content on the World Wide Web.
-
D.
SAS
SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
-
E.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24fd4777c81909ea9b9a6bd4f7ad5 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a266ed314881908b6e5e7a91930b56 |
completed | Feb. 28, 2026, 3:54 a.m. |
| NEDg | Description generation | batch_69a2677d22cc8190873d775074795a46 |
completed | Feb. 28, 2026, 3:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a267e41e148190856aa61cbb0df0ae |
completed | Feb. 28, 2026, 3:58 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.