Triple
T16966332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Senate election in Pennsylvania, 1994 |
E411546
|
entity |
| Predicate | incumbentOutcome |
P125419
|
FINISHED |
| Object | defeated |
—
|
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: defeated | Statement: [United States Senate election in Pennsylvania, 1994, incumbentOutcome, defeated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: incumbentOutcome Context triple: [United States Senate election in Pennsylvania, 1994, incumbentOutcome, defeated]
-
A.
incumbentAfterElection
Indicates that an entity holds a position or office as the sitting incumbent following a specified election.
-
B.
incumbent
Indicates that an entity currently holds a particular position, office, or role at the present time.
-
C.
incumbentBeforeElection
Indicates that the subject was already holding the relevant office or position prior to the specified election.
-
D.
numberOfIncumbents
Indicates the count of entities currently holding a particular position, role, or status at a given time.
-
E.
governorshipOutcome
Indicates the result or status of a gubernatorial contest or tenure, such as whether a candidate or officeholder won, lost, or otherwise concluded their governorship.
- 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d0a548b48190b87468630f3e3209 |
completed | April 18, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69e35d4dff4881909b384e30f2d36bff |
completed | April 18, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:31 a.m.