Triple
T30181764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GNER |
E767219
|
entity |
| Predicate | lostFranchiseDueTo |
P15139
|
FINISHED |
| Object | financial difficulties of parent company |
—
|
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: financial difficulties of parent company | Statement: [GNER, lostFranchiseDueTo, financial difficulties of parent company]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lostFranchiseDueTo Context triple: [GNER, lostFranchiseDueTo, financial difficulties of parent company]
-
A.
loserFranchise
Indicates a relationship where a sports franchise is characterized as consistently unsuccessful or underperforming, often failing to achieve significant wins or championships.
-
B.
losingFranchise
Indicates that one entity ceases to hold or control a franchise previously granted to it by another entity.
-
C.
franchiseOfLosingTeam
Indicates that one entity is the franchise to which the losing team in a given game or competition belongs.
-
D.
cityLosingFranchise
chosen
Indicates that a city is losing, or has lost, a professional sports franchise or major team based there.
-
E.
losingLeagueTeam
Indicates that a team is the one that lost in a particular league game or competition.
- 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_69f2247cc3d88190811dec3face94bf5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67f42c4708190accbdb72ae9c9816 |
completed | May 2, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69f673c7a4588190837854f3ef61e6bf |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 7:26 p.m.