Triple
T18045354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Israeli legislative election, 1973 |
E431756
|
entity |
| Predicate | seatCount_Alignment |
P63301
|
FINISHED |
| Object | 51 |
—
|
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: 51 | Statement: [Israeli legislative election, 1973, seatCount_Alignment, 51]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatCount_Alignment Context triple: [Israeli legislative election, 1973, seatCount_Alignment, 51]
-
A.
seatCount
chosen
Indicates the number of seats associated with an entity, such as a venue, vehicle, or room.
-
B.
seatNumber
Indicates the specific numbered position assigned to a seat within a defined seating arrangement or venue.
-
C.
seatStructure
Indicates that one entity serves as the structural or physical seating component or arrangement associated with another entity.
-
D.
seatingConfiguration
Indicates how seats are arranged or organized relative to each other in a given context.
-
E.
seatingPosition
Indicates the relative location or arrangement of an entity’s seat with respect to other seats or a reference point in a seating layout.
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4bff202088190ae971879348e2294 |
completed | April 19, 2026, 11:43 a.m. |
| PD | Predicate disambiguation | batch_69e3f908da508190a088aa837ea5b7af |
completed | April 18, 2026, 9:35 p.m. |
Created at: April 10, 2026, 10:25 a.m.