Triple
T516695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actor |
E10723
|
entity |
| Predicate | statuetteShape |
P15314
|
FINISHED |
| Object | knight holding a crusader’s sword standing on a film reel |
—
|
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: knight holding a crusader’s sword standing on a film reel | Statement: [Academy Award for Best Actor, statuetteShape, knight holding a crusader’s sword standing on a film reel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statuetteShape Context triple: [Academy Award for Best Actor, statuetteShape, knight holding a crusader’s sword standing on a film reel]
-
A.
hasStatue
Indicates that one entity possesses, contains, or is associated with a statue representing or located within it.
-
B.
statueHeight
Indicates the height measurement of a statue in some specified unit.
-
C.
shape
Indicates that one entity has a particular geometric or physical form characterized by the other entity.
-
D.
shaped
Indicates that one entity has given form, contour, or structure to another entity or outcome.
-
E.
medalShape
Indicates the geometric form or outline that characterizes a particular medal.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f184c3a481909bf60bb627b0ea88 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2f0151e8c81909a82b58ac0515eba |
completed | Feb. 28, 2026, 1:39 p.m. |
| PDg | Predicate description generation | batch_69a2f1137e948190838303cdaa757a5a |
completed | Feb. 28, 2026, 1:43 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.