Triple
T10865550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Segal |
E256519
|
entity |
| Predicate | sportOwnershipSector |
P71678
|
FINISHED |
| Object | association football |
—
|
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: association football | Statement: [Ted Segal, sportOwnershipSector, association football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportOwnershipSector Context triple: [Ted Segal, sportOwnershipSector, association football]
-
A.
sportsOwnershipArea
Indicates the geographic area or region over which a sports organization, team, or owner holds ownership or territorial rights.
-
B.
sportOwnershipArea
Indicates the geographic area or region over which a particular sport organization, team, or entity holds ownership or control rights.
-
C.
sportOwned
Indicates that one entity possesses ownership or control over a particular sport.
-
D.
sportInvolvedInAsOwner
Indicates that an entity has an ownership role in, or is the owner associated with, a particular sport or sports organization.
-
E.
sportIndustry
chosen
Indicates a relationship where an entity is involved in, associated with, or part of the sports industry or sports-related economic sector.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7516cebe881909ed358a7641f6a12 |
completed | April 9, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69d70d308dfc81908792f98cfb871392 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:20 p.m.