Triple
T15096234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BATRA |
E360544
|
entity |
| Predicate | associatedWithSportsIndustry |
P71678
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [BATRA, associatedWithSportsIndustry, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithSportsIndustry Context triple: [BATRA, associatedWithSportsIndustry, yes]
-
A.
otherSportIndustry
Indicates a relationship where an entity is involved in, associated with, or belongs to a sports-related industry other than the primary or specified one.
-
B.
associatedWithTeamSport
Indicates a relationship where an entity is connected to, involved in, or participates in a team-based sport.
-
C.
sportIndustry
chosen
Indicates a relationship where an entity is involved in, associated with, or part of the sports industry or sports-related economic sector.
-
D.
associatedWithSportMarket
Indicates a relationship where an entity is connected or linked to a specific sport-related market or segment within the sports industry.
-
E.
relatedSport
Indicates that there is an association or connection between an entity and a particular sport.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005466e9c8190a68e1fbeb8922b1a |
completed | April 15, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:04 a.m.