Triple
T30781031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babe Ruth’s alleged called shot in Game 3 |
E783811
|
entity |
| Predicate | hasScoreImpact |
P77094
|
FINISHED |
| Object | extended Yankees lead |
—
|
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: extended Yankees lead | Statement: [Babe Ruth’s alleged called shot in Game 3, hasScoreImpact, extended Yankees lead]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScoreImpact Context triple: [Babe Ruth’s alleged called shot in Game 3, hasScoreImpact, extended Yankees lead]
-
A.
hasImpactScale
Indicates the degree or magnitude of impact that one entity or action has on another, typically expressed along a defined scale.
-
B.
canImpact
Indicates that one entity has the potential or ability to affect, influence, or cause a change in another entity.
-
C.
scoreEffect
chosen
Indicates the impact or change that an action, event, or condition has on a score or scoring outcome.
-
D.
isScoreFor
Indicates that one value represents the score or result associated with a particular entity, event, or performance.
-
E.
usesScore
Indicates that one entity evaluates, measures, or makes decisions about another entity by applying a numerical or categorical score.
- 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_69f224b213c8819083886073f90b647e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: April 29, 2026, 8:41 p.m.