Triple
T29922003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Senate election in Connecticut, 2016 |
E759968
|
entity |
| Predicate | incumbentTermNumberSought |
P78246
|
FINISHED |
| Object | second term |
—
|
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: second term | Statement: [United States Senate election in Connecticut, 2016, incumbentTermNumberSought, second term]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: incumbentTermNumberSought Context triple: [United States Senate election in Connecticut, 2016, incumbentTermNumberSought, second term]
-
A.
numberOfIncumbents
Indicates the count of entities currently holding a particular position, role, or status at a given time.
-
B.
legislativeTermNumber
Indicates the ordinal number assigned to a specific legislative term within a sequence of legislative periods.
-
C.
incumbentPosition
Indicates that a person currently holds or occupies a specific position, office, or role.
-
D.
incumbentOffice
Indicates that a person currently holds and serves in a specified office or position.
-
E.
numberOfTermInOffice
chosen
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
- 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_69f2246189fc8190996b63ee1f9a2374 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6779474588190a1eb620e694cb7e4 |
completed | May 2, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:14 p.m.