Triple
T33221419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trent Tucker Rule |
E850430
|
entity |
| Predicate | typeOfPlayAffected |
P202635
|
FINISHED |
| Object | inbounds catch-and-shoot |
—
|
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: inbounds catch-and-shoot | Statement: [Trent Tucker Rule, typeOfPlayAffected, inbounds catch-and-shoot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfPlayAffected Context triple: [Trent Tucker Rule, typeOfPlayAffected, inbounds catch-and-shoot]
-
A.
typeOfPlayStrength
Indicates the relative intensity or power level with which a play or move is executed or characterized.
-
B.
gameplayImpact
Indicates how one element in a game affects the mechanics, difficulty, or overall experience of playing that game.
-
C.
interferedWithPlayOf
Indicates that one entity disrupted, obstructed, or otherwise negatively affected the progress or execution of another entity’s play or performance.
-
D.
partOfPlay
Indicates that one event, scene, or segment occurs as a component within a larger play or theatrical work.
-
E.
subjectOfPlay
Indicates that one entity is the main topic, theme, or focus of a play created or performed by another entity.
- 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_69f3496083dc8190b229bb6932dc548b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a00a2f0d1588190a936ea7df0ef0464 |
completed | May 10, 2026, 3:23 p.m. |
| PD | Predicate disambiguation | batch_6a00a28ccd94819085b5e123f5a4769e |
completed | May 10, 2026, 3:21 p.m. |
| PDg | Predicate description generation | batch_6a00a2f0207c81908eaaff8c2a0b3f97 |
completed | May 10, 2026, 3:23 p.m. |
Created at: May 1, 2026, 1:30 a.m.