Triple
T18837817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2023 Madhya Pradesh Legislative Assembly election |
E460709
|
entity |
| Predicate | party2SeatsWon |
P110897
|
FINISHED |
| Object | 66 |
—
|
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: 66 | Statement: [2023 Madhya Pradesh Legislative Assembly election, party2SeatsWon, 66]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: party2SeatsWon Context triple: [2023 Madhya Pradesh Legislative Assembly election, party2SeatsWon, 66]
-
A.
otherPartiesSeatsWon
Indicates the number of seats won by all parties other than the primary or main party in an election or representative body.
-
B.
numberOfSeatsWon
Indicates the quantity of seats secured by an entity (such as a party or candidate) in an election or representative body.
-
C.
oppositionPartySeatsWon
Indicates the number of legislative seats secured by the opposition party in an election or governing 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.
secondLargestPartySeatsWon
chosen
Indicates the number of seats won by the political party that finished second in size or vote share in an election.
- 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a99f443881909516a82208fd0178 |
completed | April 20, 2026, 4:20 a.m. |
| PD | Predicate disambiguation | batch_69e48d1e7dac81909ea1e758c87773c5 |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:56 a.m.