Triple
T12217940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kannauj |
E291133
|
entity |
| Predicate | legislativeAssemblySegments |
P23217
|
FINISHED |
| Object | multiple Uttar Pradesh Vidhan Sabha segments |
—
|
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: multiple Uttar Pradesh Vidhan Sabha segments | Statement: [Kannauj, legislativeAssemblySegments, multiple Uttar Pradesh Vidhan Sabha segments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legislativeAssemblySegments Context triple: [Kannauj, legislativeAssemblySegments, multiple Uttar Pradesh Vidhan Sabha segments]
-
A.
legislativeAssemblySeats
Indicates the number of seats an entity holds or is allocated in a legislative assembly.
-
B.
legislativeAssemblyConstituency
chosen
Indicates that one entity is a legislative assembly constituency associated with or represented by another entity (such as a place, jurisdiction, or legislative body).
-
C.
parliamentarySeats
Indicates the number of seats a party, group, or representative holds in a parliamentary body.
-
D.
legislatureComponent
Indicates that one entity is a constituent part or chamber of a larger legislative body.
-
E.
legislativeActivity
Indicates involvement in creating, debating, amending, or passing laws or related legislative measures.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d920e312708190b4aede2e21f5f697 |
completed | April 10, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69d91c3d669c81908eea7ad61122d275 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.