Triple
T6367961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jalgaon district |
E143273
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Amalner
Amalner is a town in the Indian state of Maharashtra known for its textile industry and as the birthplace of the Wipro company.
|
E589079
|
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: Amalner | Statement: [Jalgaon district, hasTown, Amalner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amalner Context triple: [Jalgaon district, hasTown, Amalner]
-
A.
Kharadi
Kharadi is a rapidly developing suburb in Pune, India, known as a major IT and business hub with modern infrastructure and residential complexes.
-
B.
Parbhani
Parbhani is a significant city in the Marathwada region of Maharashtra, India, known as an important commercial and educational center.
-
C.
Banavasi
Banavasi is an ancient town in Karnataka, India, historically significant as an early capital of the Kadamba dynasty and a major center of early Kannada culture and inscriptions.
-
D.
Sangamner
Sangamner is a town in the Ahmednagar district of Maharashtra, India, known as a commercial and educational hub in the northern part of the district.
-
E.
Kawardha
Kawardha is a regional dialect of the Chhattisgarhi language spoken in parts of the Indian state of Chhattisgarh.
- 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: Amalner Triple: [Jalgaon district, hasTown, Amalner]
Generated description
Amalner is a town in the Indian state of Maharashtra known for its textile industry and as the birthplace of the Wipro company.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amalner Target entity description: Amalner is a town in the Indian state of Maharashtra known for its textile industry and as the birthplace of the Wipro company.
-
A.
Kharadi
Kharadi is a rapidly developing suburb in Pune, India, known as a major IT and business hub with modern infrastructure and residential complexes.
-
B.
Parbhani
Parbhani is a significant city in the Marathwada region of Maharashtra, India, known as an important commercial and educational center.
-
C.
Banavasi
Banavasi is an ancient town in Karnataka, India, historically significant as an early capital of the Kadamba dynasty and a major center of early Kannada culture and inscriptions.
-
D.
Sangamner
Sangamner is a town in the Ahmednagar district of Maharashtra, India, known as a commercial and educational hub in the northern part of the district.
-
E.
Kawardha
Kawardha is a regional dialect of the Chhattisgarhi language spoken in parts of the Indian state of Chhattisgarh.
- 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_69c008d8c61081908bcaf61510d881ed |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c068251ae48190af8201d5f9ad35b6 |
completed | March 22, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d8689408190b334928df870ed29 |
completed | March 27, 2026, 7:11 a.m. |
| NEDg | Description generation | batch_69c6312d8a2c8190ad916969b0331170 |
completed | March 27, 2026, 7:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c63185bf28819096e518e23362a701 |
completed | March 27, 2026, 7:28 a.m. |
Created at: March 22, 2026, 4:32 p.m.