Triple
T2197805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madhubani district |
E50415
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Benipatti
Benipatti is a town in the Madhubani district of the Indian state of Bihar, known for its rural setting and proximity to the region’s famed Mithila culture.
|
E242617
|
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: Benipatti | Statement: [Madhubani district, hasTown, Benipatti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benipatti Context triple: [Madhubani district, hasTown, Benipatti]
-
A.
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.
-
B.
Vikrampur
Vikrampur was a historic urban and political center in the Bengal region, renowned as an important seat of power and culture in medieval South Asia.
-
C.
Bhagyanagar
Bhagyanagar is an old historical name for the Indian city now known as Hyderabad.
-
D.
Kalyani
Kalyani is a planned town in the Nadia district of West Bengal, India, known for its educational institutions, industries, and organized urban layout.
-
E.
Baramati
Baramati is a town in the Pune district of Maharashtra, India, known as an agricultural and industrial hub with historical and political significance.
- 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: Benipatti Triple: [Madhubani district, hasTown, Benipatti]
Generated description
Benipatti is a town in the Madhubani district of the Indian state of Bihar, known for its rural setting and proximity to the region’s famed Mithila culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Benipatti Target entity description: Benipatti is a town in the Madhubani district of the Indian state of Bihar, known for its rural setting and proximity to the region’s famed Mithila culture.
-
A.
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.
-
B.
Vikrampur
Vikrampur was a historic urban and political center in the Bengal region, renowned as an important seat of power and culture in medieval South Asia.
-
C.
Bhagyanagar
Bhagyanagar is an old historical name for the Indian city now known as Hyderabad.
-
D.
Kalyani
Kalyani is a planned town in the Nadia district of West Bengal, India, known for its educational institutions, industries, and organized urban layout.
-
E.
Baramati
Baramati is a town in the Pune district of Maharashtra, India, known as an agricultural and industrial hub with historical and political significance.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbf79f3e08190b56e9d7c0ff27237 |
completed | March 7, 2026, 6:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5db9b0208190a63a75c86ea9dcff |
completed | March 9, 2026, 5:42 a.m. |
| NEDg | Description generation | batch_69ae5e866d108190b39853172d1ed1a6 |
completed | March 9, 2026, 5:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5edfe80481908c3304c917c9065b |
completed | March 9, 2026, 5:47 a.m. |
Created at: March 4, 2026, 7:46 p.m.