Triple
T34883419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1970 United States Senate campaign in Michigan |
E1006073
|
entity |
| Predicate | outcomeForLenoreRomney |
P181973
|
FINISHED |
| Object | defeat |
—
|
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: defeat | Statement: [1970 United States Senate campaign in Michigan, outcomeForLenoreRomney, defeat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: outcomeForLenoreRomney Context triple: [1970 United States Senate campaign in Michigan, outcomeForLenoreRomney, defeat]
-
A.
outcomeForRepublicans
Indicates the result or consequence that an event, action, or situation has specifically for Republicans.
-
B.
outcomeForSarah
Indicates the result, consequence, or effect that a given situation, action, or event has specifically for Sarah.
-
C.
defeatedPresidentialCandidate
Indicates that one entity won an election against and thereby caused the loss of another entity who was running for president.
-
D.
RepublicanPrimaryOutcome
Indicates the result or status of a Republican Party primary contest, specifying which candidate or option prevailed.
-
E.
endTime (First Lady of Ohio)
Indicates the point in time at which a person’s tenure as First Lady of Ohio concludes.
- F. None of above. chosen
Provenance (4 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_69f76dbedb288190afe5780710847410 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782f4f10081908f97f6d0d2dbeec7 |
completed | May 3, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69f780ff71cc8190a67e71076fbad81a |
completed | May 3, 2026, 5:08 p.m. |
| PDg | Predicate description generation | batch_69f782f416c081908bdd9b1ad456f0e2 |
completed | May 3, 2026, 5:16 p.m. |
Created at: May 3, 2026, 4 p.m.