Triple
T16893631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Israeli legislative election, 1961 |
E424238
|
entity |
| Predicate | seatCountOfAhdutHaAvoda |
P124895
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Israeli legislative election, 1961, seatCountOfAhdutHaAvoda, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatCountOfAhdutHaAvoda Context triple: [Israeli legislative election, 1961, seatCountOfAhdutHaAvoda, 8]
-
A.
seatCount
Indicates the number of seats associated with an entity, such as a venue, vehicle, or room.
-
B.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
C.
crewAndPassengersCount
Indicates the total number of people on a vehicle or vessel, combining both crew members and passengers.
-
D.
capacityPerChair
Indicates the amount of capacity (such as weight, volume, or occupancy) that is assigned to or supported by each individual chair.
-
E.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
- F. None of above. chosen
Provenance (4 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbc6b97c8190b18aca477d6ef647 |
completed | April 18, 2026, 5:13 p.m. |
| PD | Predicate disambiguation | batch_69e32b90ec3c819099c51bb7baf2984c |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e32e2c07b081908c8fee9f5507bb9e |
completed | April 18, 2026, 7:09 a.m. |
Created at: April 10, 2026, 5:29 a.m.