Triple
T2237847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bronze Star Medal |
E49322
|
entity |
| Predicate | theaterRequirement |
P37286
|
FINISHED |
| Object | combat zone |
—
|
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: combat zone | Statement: [Bronze Star Medal, theaterRequirement, combat zone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: theaterRequirement Context triple: [Bronze Star Medal, theaterRequirement, combat zone]
-
A.
appliesToTheater
Indicates that something is relevant or applicable specifically to a theater or theatrical context.
-
B.
theaterType
Indicates the specific kind or category of theater associated with an entity (e.g., cinema, opera house, drama theater).
-
C.
filmSettingTheater
Indicates that a film’s setting or key scenes take place in a theater (such as a cinema or playhouse).
-
D.
theater
Indicates that an entity is a theater or is functioning in the role of a theater (a venue where performances or films are shown).
-
E.
theaterCommander
Indicates that an entity serves as the commanding authority over military operations within a specific theater or area of operations.
- F. None of above. chosen
Provenance (4 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc096c7748190a545cc9b229bde62 |
completed | March 7, 2026, 6:07 a.m. |
| PD | Predicate disambiguation | batch_69abbdafc07881909101266a33ae7031 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf9c77fc8190a323bcaf644fb2c5 |
completed | March 7, 2026, 6:03 a.m. |
Created at: March 4, 2026, 7:47 p.m.