Triple
T5784794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Great Train Robbery |
E128243
|
entity |
| Predicate | hasFamousShot |
P48006
|
FINISHED |
| Object | gunman firing directly at camera |
—
|
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: gunman firing directly at camera | Statement: [The Great Train Robbery, hasFamousShot, gunman firing directly at camera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamousShot Context triple: [The Great Train Robbery, hasFamousShot, gunman firing directly at camera]
-
A.
hasShootingLocation
Indicates that an audiovisual work was filmed or recorded at a particular location.
-
B.
hasPhotoSpot
Indicates that a location or entity includes or is associated with a designated place suitable for taking photographs.
-
C.
hasPhotograph
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
D.
hasMemorableMoments
chosen
Indicates that something contains or is associated with particularly notable or unforgettable moments or events.
-
E.
hasFamousSignal
Indicates that an entity is associated with a well-known or widely recognized signal, message, or indicator.
- 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_69c0084450048190bc647b649a05136b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02a19870c819084dc68b36b0cdd60 |
completed | March 22, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69c021d2cd608190b98a7e3aa7001d27 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:51 p.m.