Triple
T2900399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Foreign Legion |
E62639
|
entity |
| Predicate | uniformDistinctiveFeature |
P32310
|
FINISHED |
| Object | white kepi |
—
|
LITERAL 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: white kepi | Statement: [Foreign Legion, uniformDistinctiveFeature, white kepi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: uniformDistinctiveFeature Context triple: [Foreign Legion, uniformDistinctiveFeature, white kepi]
-
A.
uniformDistinction
Indicates that a clear and consistent difference is maintained between two or more entities within a given context.
-
B.
hasDistinctFeature
Indicates that an entity possesses a specific characteristic or attribute that differentiates it from others.
-
C.
distinctiveMarking
chosen
Indicates that one entity bears a unique or distinguishing visual feature or pattern that sets it apart from others.
-
D.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
-
E.
hasDistinctLettersFor
Indicates that one entity is associated with another such that the letters used in the first are all different from (i.e., share no letters with) those used in the second.
- F. None of above.
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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0b081308190af8875151fb11c4e |
completed | March 7, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69abdd19bac881908f047d616aca8438 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:10 p.m.