Triple
T19503840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Governor of Pennsylvania |
E487970
|
entity |
| Predicate | electedOn |
P4036
|
FINISHED |
| Object | same ticket as Governor of Pennsylvania |
—
|
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: same ticket as Governor of Pennsylvania | Statement: [Lieutenant Governor of Pennsylvania, electedOn, same ticket as Governor of Pennsylvania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: electedOn Context triple: [Lieutenant Governor of Pennsylvania, electedOn, same ticket as Governor of Pennsylvania]
-
A.
electedWithin
Indicates that one entity was chosen or appointed to a position, office, or role within the jurisdiction, boundaries, or organizational scope of another entity.
-
B.
electedAfter
Indicates that one entity was elected to a position at a later time than another entity.
-
C.
electsAt
Indicates that an election or selection of someone or something to a position, role, or office occurs at a specific time or in a specific context.
-
D.
electedWith
chosen
Indicates that one entity attained an elected position or office together with, or as part of the same electoral outcome as, another entity.
-
E.
electionsHeldOn
Indicates that one or more elections took place on a specified date or set of dates.
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e635105db8819084915dc2d047188d |
completed | April 20, 2026, 2:15 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7bd25881908caa04eaef1f6718 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:40 p.m.