Triple
T8601007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belagavi district |
E203672
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Khanapur
Khanapur is a town in the Indian state of Karnataka known for its scenic surroundings, forested areas, and proximity to the Western Ghats.
|
E744802
|
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: Khanapur | Statement: [Belagavi district, containsTown, Khanapur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Khanapur Context triple: [Belagavi district, containsTown, Khanapur]
-
A.
Madhepur
Madhepur is a town located in the Madhubani district of the Indian state of Bihar.
-
B.
Tuljapur
Tuljapur is a town in Maharashtra, India, renowned as a major pilgrimage center for the goddess Bhavani.
-
C.
Kushalnagar
Kushalnagar is a town in Karnataka’s Kodagu district, known as a gateway to the Coorg region and nearby river, dam, and nature attractions.
-
D.
Kopargaon
Kopargaon is a town in the Ahmednagar district of Maharashtra, India, known as a commercial and transport hub near the pilgrimage city of Shirdi.
-
E.
Nagole
Nagole is a residential and commercial neighborhood in Hyderabad, India, served as a key terminus and transit hub on the Hyderabad Metro network.
- 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: Khanapur Triple: [Belagavi district, containsTown, Khanapur]
Generated description
Khanapur is a town in the Indian state of Karnataka known for its scenic surroundings, forested areas, and proximity to the Western Ghats.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Khanapur Target entity description: Khanapur is a town in the Indian state of Karnataka known for its scenic surroundings, forested areas, and proximity to the Western Ghats.
-
A.
Madhepur
Madhepur is a town located in the Madhubani district of the Indian state of Bihar.
-
B.
Tuljapur
Tuljapur is a town in Maharashtra, India, renowned as a major pilgrimage center for the goddess Bhavani.
-
C.
Kushalnagar
Kushalnagar is a town in Karnataka’s Kodagu district, known as a gateway to the Coorg region and nearby river, dam, and nature attractions.
-
D.
Kopargaon
Kopargaon is a town in the Ahmednagar district of Maharashtra, India, known as a commercial and transport hub near the pilgrimage city of Shirdi.
-
E.
Nagole
Nagole is a residential and commercial neighborhood in Hyderabad, India, served as a key terminus and transit hub on the Hyderabad Metro network.
- 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_69ca832b56948190ba751cec255308f1 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46d8ff408190acc7cd8dc99b2689 |
completed | March 31, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea8f13f7081908317c1b2d87a51b2 |
completed | April 2, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_69cea9d0dad0819095134f6f8cafb4c0 |
completed | April 2, 2026, 5:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ceaa7025388190a3f17aca46d4858e |
completed | April 2, 2026, 5:42 p.m. |
Created at: March 30, 2026, 6:24 p.m.