Triple
T16508886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pim Fortuyn List |
E401002
|
entity |
| Predicate | seatsWon |
P31127
|
FINISHED |
| Object | 26 seats in the House of Representatives (2002) |
—
|
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: 26 seats in the House of Representatives (2002) | Statement: [Pim Fortuyn List, seatsWon, 26 seats in the House of Representatives (2002)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatsWon Context triple: [Pim Fortuyn List, seatsWon, 26 seats in the House of Representatives (2002)]
-
A.
numberOfSeatsWon
chosen
Indicates the quantity of seats secured by an entity (such as a party or candidate) in an election or representative body.
-
B.
speakerSeatsWon
Indicates the number of seats won by the entity serving or designated as the speaker in a given election or legislative context.
-
C.
seatsWonByBalad
Indicates the number of legislative seats that were won by the Balad party in an election.
-
D.
otherPartiesSeatsWon
Indicates the number of seats won by all parties other than the primary or main party in an election or representative body.
-
E.
oppositionPartySeatsWon
Indicates the number of legislative seats secured by the opposition party in an election or governing body.
- 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e54331c8190b3c4f9de95cbbc5e |
completed | April 18, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69e296995d388190b88ebe189dce890d |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:14 a.m.