Triple
T2462250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kathryn Bigelow |
E54556
|
entity |
| Predicate | notableSubject |
P4941
|
FINISHED |
| Object | war on terror |
—
|
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: war on terror | Statement: [Kathryn Bigelow, notableSubject, war on terror]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableSubject Context triple: [Kathryn Bigelow, notableSubject, war on terror]
-
A.
subjectNotableFor
Indicates that the subject is especially recognized or distinguished for a particular attribute, achievement, role, or characteristic.
-
B.
notableFor
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
C.
notableWorkSubject
chosen
Indicates that a work is notably associated with a particular subject, such as a person, topic, or entity, as its primary focus or theme.
-
D.
notableCategory
Indicates that an entity is recognized as notable or significant within a particular category or classification.
-
E.
notableDuring
Indicates that something was especially prominent, active, or significant during a particular time period or event.
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd49c5aa081909ab4f726a458b77f |
completed | March 7, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69abd0b199488190aa381b36593ae1ac |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.