Triple
T13071136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auraiya district |
E329457
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Dibiyapur
Dibiyapur is a small industrial town in the Auraiya district of Uttar Pradesh, India, known for its power and gas-based industries.
|
E1021419
|
NE FINISHED |
How this triple was built (4 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: Dibiyapur | Statement: [Auraiya district, hasTown, Dibiyapur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dibiyapur Context triple: [Auraiya district, hasTown, Dibiyapur]
-
A.
Babatpur
Babatpur is a locality near Varanasi in the Indian state of Uttar Pradesh, known primarily for hosting the city’s main airport.
-
B.
Jangipur
Jangipur is a town in the Murshidabad district of the Indian state of West Bengal, known for its administrative significance and proximity to the Ganges River.
-
C.
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.
-
D.
Baruipur
Baruipur is a suburban town and municipality in West Bengal, India, known as an important residential and commercial hub near Kolkata.
-
E.
Bhatkuli
Bhatkuli is a small town in the Amravati district of Maharashtra, India, known primarily as a local administrative and market center for surrounding rural areas.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Dibiyapur Triple: [Auraiya district, hasTown, Dibiyapur]
Generated description
Dibiyapur is a small industrial town in the Auraiya district of Uttar Pradesh, India, known for its power and gas-based industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dibiyapur Target entity description: Dibiyapur is a small industrial town in the Auraiya district of Uttar Pradesh, India, known for its power and gas-based industries.
-
A.
Babatpur
Babatpur is a locality near Varanasi in the Indian state of Uttar Pradesh, known primarily for hosting the city’s main airport.
-
B.
Jangipur
Jangipur is a town in the Murshidabad district of the Indian state of West Bengal, known for its administrative significance and proximity to the Ganges River.
-
C.
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.
-
D.
Baruipur
Baruipur is a suburban town and municipality in West Bengal, India, known as an important residential and commercial hub near Kolkata.
-
E.
Bhatkuli
Bhatkuli is a small town in the Amravati district of Maharashtra, India, known primarily as a local administrative and market center for surrounding rural areas.
- F. None of above. chosen
Provenance (5 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980ee6130819095d835e7ff6a8c5b |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e26e5d6881908663444bca67b01e |
completed | May 3, 2026, 5:51 a.m. |
| NEDg | Description generation | batch_69f6e32bf5508190b4dc58971f8f64d0 |
completed | May 3, 2026, 5:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6e407dd988190b928b8931985a815 |
completed | May 3, 2026, 5:58 a.m. |
Created at: April 9, 2026, 9 p.m.