Triple
T37810664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kentucky's 3rd congressional district |
E942631
|
entity |
| Predicate | numberOfHouseSeatsInState |
P4273
|
FINISHED |
| Object | 1 of 6 U.S. House districts in Kentucky |
—
|
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: 1 of 6 U.S. House districts in Kentucky | Statement: [Kentucky's 3rd congressional district, numberOfHouseSeatsInState, 1 of 6 U.S. House districts in Kentucky]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfHouseSeatsInState Context triple: [Kentucky's 3rd congressional district, numberOfHouseSeatsInState, 1 of 6 U.S. House districts in Kentucky]
-
A.
numberOfSeatsInSenate
Indicates the total count of seats allocated in a given senate.
-
B.
numberOfRepresentatives
chosen
Indicates the quantity of representatives associated with a given entity or unit.
-
C.
numberOfSenateDistricts
Indicates the total count of senate districts associated with a given entity or jurisdiction.
-
D.
numberOfTermsInHouse
Indicates the total count of terms an individual has served in a legislative house.
-
E.
hasLowerHouseSeats
Indicates the number of seats an entity holds in the lower house of a bicameral legislature.
- 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_69f76ee8104c8190ab17133ccd8f86e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:19 p.m.