Triple
T34910990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 Connecticut gubernatorial election |
E1006863
|
entity |
| Predicate | mainOpponentFullName |
P126969
|
FINISHED |
| Object | Thomas C. Foley |
—
|
NE NERFINISHED |
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: Thomas C. Foley | Statement: [2014 Connecticut gubernatorial election, mainOpponentFullName, Thomas C. Foley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainOpponentFullName Context triple: [2014 Connecticut gubernatorial election, mainOpponentFullName, Thomas C. Foley]
-
A.
opponentFullName
chosen
Indicates the full name of an entity’s opponent in a competitive or adversarial context.
-
B.
opponentNickname
Indicates that one entity is the nickname used to refer to another entity in a competitive or adversarial context.
-
C.
opponentTitle
Indicates that the object is the formal title or designation held by an opponent in a competitive or adversarial context.
-
D.
laterOpponent
Indicates that one entity becomes the opponent of another at a later time or stage, following an earlier phase or matchup.
-
E.
opponentDescriptor
Indicates how an opposing party in a conflict, competition, or interaction is characterized or described in relation to another entity.
- 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_69f76dc1b4a081909b4c6e4d8ec0aa2d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f791cc969c8190bf187d6031a030d5 |
completed | May 3, 2026, 6:19 p.m. |
| PD | Predicate disambiguation | batch_69f791033d288190b118029fe412b9c9 |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4 p.m.