Triple
T12697509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kibō no Tō |
E303373
|
entity |
| Predicate | numberOfSeatsWonIn2017GeneralElection |
P31127
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [Kibō no Tō, numberOfSeatsWonIn2017GeneralElection, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeatsWonIn2017GeneralElection Context triple: [Kibō no Tō, numberOfSeatsWonIn2017GeneralElection, 50]
-
A.
numberOfSeatsWonIn2019ParliamentaryElection
Indicates the number of seats an entity won in the 2019 parliamentary election.
-
B.
seatsWonIn2019ParliamentaryElection
Indicates the number of seats an entity secured in the 2019 parliamentary election.
-
C.
LabourSeatsWon
Indicates the number of parliamentary seats won by the Labour Party in an election.
-
D.
numberOfSeatsWon
chosen
Indicates the quantity of seats secured by an entity (such as a party or candidate) in an election or representative body.
-
E.
speakerSeatsWon
Indicates the number of seats won by the entity serving or designated as the speaker in a given election or legislative context.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d962a32c6481908ddaddae4ea267bf |
completed | April 10, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69d960be63f081908a5ef5ef17a311bf |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:22 p.m.