Triple
T6408761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OASIS |
E127653
|
entity |
| Predicate | standardDeveloped |
P1371
|
FINISHED |
| Object | DITA |
E242854
|
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: DITA | Statement: [OASIS, standardDeveloped, DITA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DITA Context triple: [OASIS, standardDeveloped, DITA]
-
A.
DITA
chosen
DITA (Darwin Information Typing Architecture) is an XML-based standard for authoring, structuring, and publishing modular technical documentation.
-
B.
DTD
DTD (Document Type Definition) is an XML schema language used to define the legal structure, elements, and attributes of an XML document.
-
C.
DTD
DTD is the abbreviated name for the Division of Training and Development, an organizational unit focused on designing and delivering training and professional development programs.
-
D.
DocBook
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.
-
E.
DIT
DIT is the South Australian government department responsible for planning, developing, and managing the state’s transport systems and infrastructure.
- 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.