Triple
T28702477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burari Assembly constituency |
E729590
|
entity |
| Predicate | hasMLA |
P133573
|
FINISHED |
| Object | Sanjeev Jha |
—
|
NE NERFINISHED |
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: Sanjeev Jha | Statement: [Burari Assembly constituency, hasMLA, Sanjeev Jha]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMLA Context triple: [Burari Assembly constituency, hasMLA, Sanjeev Jha]
-
A.
currentMLA
Indicates that the subject is the person who currently serves as the Member of the Legislative Assembly (MLA) for the object’s electoral district.
-
B.
previousMLA
Indicates that one entity served as a Member of the Legislative Assembly (MLA) for a constituency or jurisdiction immediately before the other entity.
-
C.
hadMLA
chosen
Indicates that an entity was represented by, or had as its legislative representative, a particular Member of the Legislative Assembly (MLA).
-
D.
hadMLAParty
Indicates that an entity hosted or participated in a party or social gathering associated with the MLA (e.g., during an MLA conference or event).
-
E.
MLA2022Party
Indicates a relationship where an entity is associated with, participates in, or is recognized as part of the MLA 2022 party event or gathering.
- 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_69f043e6e9688190b6bdd6e5665498ff |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f656b5405881908b22cbcf723bff61 |
completed | May 2, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69f651ac855481908e30c3b345d31356 |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 28, 2026, 5:43 a.m.