Triple
T34317955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 Israeli legislative election |
E880645
|
entity |
| Predicate | seatsWonByKulanu |
P180287
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [2015 Israeli legislative election, seatsWonByKulanu, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatsWonByKulanu Context triple: [2015 Israeli legislative election, seatsWonByKulanu, 10]
-
A.
seatsWonByBalad
Indicates the number of legislative seats that were won by the Balad party in an election.
-
B.
numberOfSeatsWon
Indicates the quantity of seats secured by an entity (such as a party or candidate) in an election or representative body.
-
C.
seatsWonByCUP
Indicates the number of seats that were won by the CUP party in an election or legislative body.
-
D.
speakerSeatsWon
Indicates the number of seats won by the entity serving or designated as the speaker in a given election or legislative context.
-
E.
leader4SeatsWon
Indicates the number of seats won that are attributed to or associated with a particular leader.
- 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_69f349b9cd508190a996a616903b3e6d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
| PDg | Predicate description generation | batch_69f739a58b3c81908abc2b8738a65678 |
completed | May 3, 2026, 12:03 p.m. |
Created at: May 1, 2026, 1:57 a.m.