Triple
T32281583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gary Cooper as Tom Brown |
E824706
|
entity |
| Predicate | militaryUnitInFiction |
P145031
|
FINISHED |
| Object | French Foreign Legion |
—
|
NE NERFINISHED |
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: French Foreign Legion | Statement: [Gary Cooper as Tom Brown, militaryUnitInFiction, French Foreign Legion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: militaryUnitInFiction Context triple: [Gary Cooper as Tom Brown, militaryUnitInFiction, French Foreign Legion]
-
A.
portraysMilitaryUnit
Indicates that one entity visually or narratively represents, depicts, or shows a specific military unit.
-
B.
militaryUnitName
Indicates the specific official name assigned to a military unit in the context of a broader relationship or record.
-
C.
militaryUnitOf
Indicates that one entity is a military unit that belongs to, is part of, or is under the command or organizational structure of another entity.
-
D.
notableMilitaryUnit
Indicates that an entity is a military unit that holds particular significance, prominence, or recognition in some context.
-
E.
featuresFictionalUnit
chosen
Indicates that something includes or presents a fictional unit (such as a character, group, or organization) as part of its content or structure.
- 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_69f3490f404081908450db66884f4334 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 1, 2026, 12:43 a.m.