Triple
T6408760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OASIS |
E127653
|
entity |
| Predicate | standardDeveloped |
P1371
|
FINISHED |
| Object | DocBook |
E48511
|
NE FINISHED |
How this triple was built (2 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: DocBook | Statement: [OASIS, standardDeveloped, DocBook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DocBook Context triple: [OASIS, standardDeveloped, DocBook]
-
A.
DocBook
chosen
DocBook is a semantic markup language, originally based on SGML and now commonly used in XML form, designed for authoring and publishing technical documentation and books in a platform-independent way.
-
B.
DSSSL
DSSSL (Document Style Semantics and Specification Language) is an ISO standard language used to define stylesheets and transformations for SGML documents, particularly in technical publishing.
-
C.
DITA
DITA (Darwin Information Typing Architecture) is an XML-based standard for authoring, structuring, and publishing modular technical documentation.
-
D.
SGML
SGML (Standard Generalized Markup Language) is a standardized metalanguage for defining markup languages used to structure and describe the content of electronic documents.
-
E.
OpenDocument format
OpenDocument format is an open, XML-based file format standard for office documents such as text, spreadsheets, and presentations, designed for interoperability across different software suites.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c0083723d88190b1e37b19df162c08 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068cdf25881908d42a5d979637ad6 |
completed | March 22, 2026, 10:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c638b38c888190aa2433173db64c90 |
completed | March 27, 2026, 7:58 a.m. |
Created at: March 22, 2026, 4:41 p.m.