Triple
T21908510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sikkim Legislative Assembly |
E541001
|
entity |
| Predicate | hasTotalSeats |
P55873
|
FINISHED |
| Object | 32 |
—
|
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: 32 | Statement: [Sikkim Legislative Assembly, hasTotalSeats, 32]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalSeats Context triple: [Sikkim Legislative Assembly, hasTotalSeats, 32]
-
A.
currentNumberOfSeats
chosen
Indicates the present total count of seats associated with an entity or context.
-
B.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
-
C.
numberOfListSeats
Indicates the total count of seats allocated from a party or group list within a representative body or election system.
-
D.
hasGeneralSeats
Indicates that an entity possesses or includes general (non-reserved) seats in a seating or allocation context.
-
E.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f121d806688190b23502aacbfde4bd |
completed | April 28, 2026, 9:08 p.m. |
| PD | Predicate disambiguation | batch_69e6be9ebf4c8190892df1a8e1313f88 |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 7:39 p.m.