Triple
T12117678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ram Manohar Lohia |
E288606
|
entity |
| Predicate | birthplace |
P1
|
FINISHED |
| Object | Akbarpur |
E288606
|
NE 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: Akbarpur | Statement: [Ram Manohar Lohia, birthplace, Akbarpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akbarpur Context triple: [Ram Manohar Lohia, birthplace, 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.
Daryapur
Daryapur is a town in the Amravati district of Maharashtra, India, known for its agricultural economy and regional market activities.
-
C.
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.
-
D.
Mahipalpur
Mahipalpur is an urban village and commercial area in Delhi, India, located near Indira Gandhi International Airport and known for its hotels, transport hubs, and proximity to major highways.
-
E.
Partapur
Partapur is a locality in Meerut district of Uttar Pradesh, India, known for its proximity to the Dr. Bhimrao Ambedkar Airstrip and its growing urban and institutional development.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915760d208190b68f5e024b3676ba |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a75f77881908345a585a689f69f |
completed | May 2, 2026, 2:30 p.m. |
Created at: April 8, 2026, 9:49 p.m.