Triple
T19018169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shah Alam I |
E465411
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Burhanpur |
—
|
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: Burhanpur | Statement: [Shah Alam I, birthPlace, Burhanpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burhanpur Context triple: [Shah Alam I, birthPlace, Burhanpur]
-
A.
Burhanpur
chosen
Burhanpur is a historic city in central India known for its Mughal-era architecture and strategic location on the banks of the Tapti River.
-
B.
Baharampur
Baharampur is a major town and administrative center in the Murshidabad district of the Indian state of West Bengal, known for its historical significance and regional commerce.
-
C.
Narayanpur
Narayanpur is a town located in the Lakhimpur district of the Indian state of Assam.
-
D.
Babatpur
Babatpur is a locality near Varanasi in the Indian state of Uttar Pradesh, known primarily for hosting the city’s main airport.
-
E.
Mahidpur
Mahidpur is a historic town in the Indian state of Madhya Pradesh, known for its location in the Malwa region and its role in the Anglo-Maratha conflicts.
- 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_69d8dd025c188190a1d81f5b4ec7e2c6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6dd0e6c8190a6dc6af1f7901299 |
completed | April 20, 2026, 7:33 a.m. |
Created at: April 10, 2026, 12:02 p.m.