Triple
T2640483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Tyree |
E62852
|
entity |
| Predicate | typeOfPlay |
P42437
|
FINISHED |
| Object | improvised deep pass reception |
—
|
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: improvised deep pass reception | Statement: [David Tyree, typeOfPlay, improvised deep pass reception]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfPlay Context triple: [David Tyree, typeOfPlay, improvised deep pass reception]
-
A.
theatreType
Indicates the specific category or kind of theatre associated with an entity, such as its format, style, or operational model.
-
B.
dramaticForm
Indicates that one entity is expressed, structured, or realized in the form of a particular dramatic genre or theatrical mode.
-
C.
theaterType
Indicates the specific kind or category of theater associated with an entity (e.g., cinema, opera house, drama theater).
-
D.
theaterWork
Indicates a relationship where an entity is a theatrical work (such as a play or stage production) associated with another entity, typically as its subject, creator, or context.
-
E.
theatreOf
Indicates that an event, action, or operation takes place within or is primarily associated with a particular theatre or venue.
- 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd8fc8ee881908a9f6820d8934a62 |
completed | March 7, 2026, 7:51 a.m. |
| PD | Predicate disambiguation | batch_69abd812849881908f956845a80e0205 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8abcc348190bfa05c0abc4bcee7 |
completed | March 7, 2026, 7:50 a.m. |
Created at: March 6, 2026, 9:53 p.m.