Triple
T3486233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Assembly of Nepal |
E73611
|
entity |
| Predicate | seatsReservedForWomen |
P9399
|
FINISHED |
| Object | at least 3 from each province |
—
|
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: at least 3 from each province | Statement: [National Assembly of Nepal, seatsReservedForWomen, at least 3 from each province]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatsReservedForWomen Context triple: [National Assembly of Nepal, seatsReservedForWomen, at least 3 from each province]
-
A.
hasReservedSeats
chosen
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
-
B.
hasGeneralSeats
Indicates that an entity possesses or includes general (non-reserved) seats in a seating or allocation context.
-
C.
seatsAllocatedBy
Indicates that seats are assigned or distributed by a particular agent, authority, or mechanism.
-
D.
seatsForScheduledCastes
Indicates that a certain number or portion of seats are reserved specifically for individuals belonging to Scheduled Castes.
-
E.
womenStatus
Indicates the social, legal, economic, or cultural position or condition assigned to women within a given context or system.
- 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_69ad85cca8d4819088494e9f3340fab5 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbb9059f881908f9cbe544365c8df |
completed | March 8, 2026, 6:10 p.m. |
| PD | Predicate disambiguation | batch_69adae0935ac8190bfa8a8bd3dcd3301 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.