Triple
T16474220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patan district |
E372441
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Santalpur
Santalpur is a small town in the Patan district of Gujarat, India, known primarily as a local administrative and trading center for surrounding rural areas.
|
E1228456
|
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: Santalpur | Statement: [Patan district, containsTown, Santalpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santalpur Context triple: [Patan district, containsTown, Santalpur]
-
A.
Samastipur
Samastipur is a city in the Indian state of Bihar known as an important railway junction and agricultural trade center in the region.
-
B.
Karanpur
Karanpur is a town located in the Ganganagar district of the northern Indian state of Rajasthan.
-
C.
Ellichpur
Ellichpur is a historic city in Maharashtra, India, that once served as an important regional capital under the Deccan sultanates.
-
D.
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.
-
E.
Srikaranpur
Srikaranpur is a town in the northern Indian state of Rajasthan, situated near the India–Pakistan border in the agriculturally rich Ganganagar region.
- 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: Santalpur Triple: [Patan district, containsTown, Santalpur]
Generated description
Santalpur is a small town in the Patan district of Gujarat, India, known primarily as a local administrative and trading center for surrounding rural areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Santalpur Target entity description: Santalpur is a small town in the Patan district of Gujarat, India, known primarily as a local administrative and trading center for surrounding rural areas.
-
A.
Samastipur
Samastipur is a city in the Indian state of Bihar known as an important railway junction and agricultural trade center in the region.
-
B.
Karanpur
Karanpur is a town located in the Ganganagar district of the northern Indian state of Rajasthan.
-
C.
Ellichpur
Ellichpur is a historic city in Maharashtra, India, that once served as an important regional capital under the Deccan sultanates.
-
D.
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.
-
E.
Srikaranpur
Srikaranpur is a town in the northern Indian state of Rajasthan, situated near the India–Pakistan border in the agriculturally rich Ganganagar region.
- 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32dd32e048190a9eadd32d6b9374c |
completed | April 18, 2026, 7:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a2363208190beb218e633d0627e |
completed | May 10, 2026, 1:37 p.m. |
| NEDg | Description generation | batch_6a008c8adc188190adc6e85cc4c9f93a |
completed | May 10, 2026, 1:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a008ce668f88190b80dc541262ae0f5 |
completed | May 10, 2026, 1:49 p.m. |
Created at: April 10, 2026, 5:13 a.m.