Triple
T19838087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mau |
E476650
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Ghazipur |
—
|
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: Ghazipur | Statement: [Mau, locatedNear, Ghazipur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ghazipur Context triple: [Mau, locatedNear, Ghazipur]
-
A.
Ghazipur
chosen
Ghazipur is a city in the Indian state of Uttar Pradesh, known for its historical significance and as a regional hub in eastern Uttar Pradesh.
-
B.
Farrukhabad
Farrukhabad is a city and parliamentary constituency in the Indian state of Uttar Pradesh, known historically for its trade and cultural significance.
-
C.
Shahjahanpur
Shahjahanpur is a prominent city in the Rohilkhand region of Uttar Pradesh, India, known for its historical significance and regional commercial importance.
-
D.
Unnao
Unnao is a city in northern India known for its historical significance and its location between the major urban centers of Lucknow and Kanpur in Uttar Pradesh.
-
E.
Chandauli
Chandauli is a town and administrative district headquarters in the eastern Indian state of Uttar Pradesh, known for its agricultural economy and proximity to Varanasi.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65803bb988190a4e7c0035058feba |
completed | April 20, 2026, 4:44 p.m. |
Created at: April 10, 2026, 1:50 p.m.