Triple
T14532062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jhansi district |
E340939
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Mauranipur
Mauranipur is a town in the Jhansi district of Uttar Pradesh, India, known for its historical significance and regional cultural heritage.
|
E1105666
|
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: Mauranipur | Statement: [Jhansi district, containsTown, Mauranipur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mauranipur Context triple: [Jhansi district, containsTown, Mauranipur]
-
A.
Narayanpur
Narayanpur is a town located in the Lakhimpur district of the Indian state of Assam.
-
B.
Bikapur
Bikapur is a town and administrative subdivision in the Ayodhya district of Uttar Pradesh, India, known for its proximity to the historic city of Ayodhya.
-
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.
Babatpur
Babatpur is a locality near Varanasi in the Indian state of Uttar Pradesh, known primarily for hosting the city’s main airport.
-
E.
Dibiyapur
Dibiyapur is a small industrial town in the Auraiya district of Uttar Pradesh, India, known for its power and gas-based industries.
- 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: Mauranipur Triple: [Jhansi district, containsTown, Mauranipur]
Generated description
Mauranipur is a town in the Jhansi district of Uttar Pradesh, India, known for its historical significance and regional cultural heritage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mauranipur Target entity description: Mauranipur is a town in the Jhansi district of Uttar Pradesh, India, known for its historical significance and regional cultural heritage.
-
A.
Narayanpur
Narayanpur is a town located in the Lakhimpur district of the Indian state of Assam.
-
B.
Bikapur
Bikapur is a town and administrative subdivision in the Ayodhya district of Uttar Pradesh, India, known for its proximity to the historic city of Ayodhya.
-
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.
Babatpur
Babatpur is a locality near Varanasi in the Indian state of Uttar Pradesh, known primarily for hosting the city’s main airport.
-
E.
Dibiyapur
Dibiyapur is a small industrial town in the Auraiya district of Uttar Pradesh, India, known for its power and gas-based industries.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea052d01c81909c8592c351be6f35 |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ab24f8c8190bb0e68ebb854844d |
completed | May 8, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_69fd8b686ef081908b3f3ddedde12685 |
completed | May 8, 2026, 7:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd8c9920108190ae4eea3e1d990ea2 |
completed | May 8, 2026, 7:11 a.m. |
Created at: April 10, 2026, 1:22 a.m.