Triple
T26957184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mordecai Brown |
E678938
|
entity |
| Predicate | benefitOfInjury |
P27270
|
FINISHED |
| Object | enhanced grip and movement on curveball |
—
|
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: enhanced grip and movement on curveball | Statement: [Mordecai Brown, benefitOfInjury, enhanced grip and movement on curveball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitOfInjury Context triple: [Mordecai Brown, benefitOfInjury, enhanced grip and movement on curveball]
-
A.
benefitProtection
Indicates that one entity provides protective advantages or safeguards that benefit another entity.
-
B.
safetyBenefit
Indicates that one entity provides, contributes to, or results in an improvement in the safety or risk reduction experienced by another entity.
-
C.
benefitsAre
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
D.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
E.
benefitsCause
chosen
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or cause.
- 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_69eeeb4e75f08190b14fc91ca4a91488 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f65aa07c048190a5df30d53d8f0cf5 |
completed | May 2, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69f659cc571c819097e51e531961d812 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 27, 2026, 6:28 a.m.