Triple
T2175263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DocBook |
E48511
|
entity |
| Predicate | supportsOutputFormat |
P23634
|
FINISHED |
| Object | RTF |
E184240
|
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: RTF | Statement: [DocBook, supportsOutputFormat, RTF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RTF Context triple: [DocBook, supportsOutputFormat, RTF]
-
A.
Rich Text Format
chosen
Rich Text Format (RTF) is a cross-platform document file format developed by Microsoft that preserves basic text formatting and structure while remaining readable by many word processors.
-
B.
Word
Word is Microsoft’s widely used word processing application for creating, editing, and formatting text documents.
-
C.
WordPad
WordPad is a basic word processing application for Microsoft Windows that offers more features than Notepad but fewer than full office suites like Microsoft Word.
-
D.
RT
RT is a Russian state-funded international television network and online media outlet known for its global news coverage and often controversial, Kremlin-aligned perspectives on major events.
-
E.
TAR
TAR is the ICAO airline designator assigned to Tunisair, the national flag carrier of Tunisia.
- 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_69a88aa3faa48190995b233af6525815 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc5af20808190902031d8c0bba376 |
completed | March 7, 2026, 6:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d9eff988190a02734bd73616cba |
completed | March 9, 2026, 5:41 a.m. |
Created at: March 4, 2026, 7:45 p.m.