Triple
T19731362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faizabad division |
E473860
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Akbarpur |
—
|
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: Akbarpur | Statement: [Faizabad division, hasSettlement, Akbarpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akbarpur Context triple: [Faizabad division, hasSettlement, Akbarpur]
-
A.
Akbarpur
chosen
Akbarpur is a town in the Indian state of Uttar Pradesh known as the birthplace of socialist leader Ram Manohar Lohia.
-
B.
Sahaspur
Sahaspur is a legislative assembly constituency located in the Dehradun district of the Indian state of Uttarakhand.
-
C.
Daryapur
Daryapur is a town in the Amravati district of Maharashtra, India, known for its agricultural economy and regional market activities.
-
D.
Sikandarpur
Sikandarpur is a metro station in the Delhi Metro network that serves the Gurugram area and provides an interchange with the Rapid Metro system.
-
E.
Mubarakpur
Mubarakpur is a town in Uttar Pradesh, India, known for its traditional handloom weaving and production of fine silk sarees.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e649fd18148190a6e85b2be0069dde |
completed | April 20, 2026, 3:45 p.m. |
Created at: April 10, 2026, 1:47 p.m.