Triple
T19345149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Senate seat from Kansas |
E483856
|
entity |
| Predicate | numberOfSeatsForKansasInSenate |
P48166
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [United States Senate seat from Kansas, numberOfSeatsForKansasInSenate, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeatsForKansasInSenate Context triple: [United States Senate seat from Kansas, numberOfSeatsForKansasInSenate, 2]
-
A.
numberOfSeatsInSenate
chosen
Indicates the total count of seats allocated in a given senate.
-
B.
numberOfSenateDistricts
Indicates the total count of senate districts associated with a given entity or jurisdiction.
-
C.
numberOfSenates
Indicates the total count of senate bodies associated with or present in a given context or entity.
-
D.
numberOfSenators
Indicates the total count of senators associated with a given political body, region, or entity.
-
E.
numberOfIndependentSenators
Indicates the count of senators who are not formally affiliated with any political party.
- 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6185a56b4819089336564959b84df |
completed | April 20, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:34 p.m.