Triple
T19395374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Etah |
E485174
|
entity |
| Predicate | distanceTo |
P350
|
FINISHED |
| Object |
Kasganj
Kasganj is a town and district headquarters in the Indian state of Uttar Pradesh, known for its agricultural trade and regional connectivity.
|
E1384383
|
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: Kasganj | Statement: [Etah, distanceTo, Kasganj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kasganj Context triple: [Etah, distanceTo, Kasganj]
-
A.
Karauli
Karauli is a historic town and pilgrimage center in the Indian state of Rajasthan, known for its ancient temples and distinctive red sandstone architecture.
-
B.
Hoshangabad
Hoshangabad is a city in the Indian state of Madhya Pradesh, known for its location on the banks of the Narmada River and its agricultural and industrial activities.
-
C.
Vikasnagar
Vikasnagar is a town in the Indian state of Uttarakhand known as a commercial and agricultural hub near the foothills of the Himalayas.
-
D.
Nakodar
Nakodar is a prominent town in the Indian state of Punjab, known for its historical significance and cultural heritage within the Jalandhar region.
-
E.
Tekanpur
Tekanpur is a town in Madhya Pradesh, India, best known for hosting the Border Security Force’s main training academy.
- 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: Kasganj Triple: [Etah, distanceTo, Kasganj]
Generated description
Kasganj is a town and district headquarters in the Indian state of Uttar Pradesh, known for its agricultural trade and regional connectivity.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kasganj Target entity description: Kasganj is a town and district headquarters in the Indian state of Uttar Pradesh, known for its agricultural trade and regional connectivity.
-
A.
Karauli
Karauli is a historic town and pilgrimage center in the Indian state of Rajasthan, known for its ancient temples and distinctive red sandstone architecture.
-
B.
Hoshangabad
Hoshangabad is a city in the Indian state of Madhya Pradesh, known for its location on the banks of the Narmada River and its agricultural and industrial activities.
-
C.
Vikasnagar
Vikasnagar is a town in the Indian state of Uttarakhand known as a commercial and agricultural hub near the foothills of the Himalayas.
-
D.
Nakodar
Nakodar is a prominent town in the Indian state of Punjab, known for its historical significance and cultural heritage within the Jalandhar region.
-
E.
Tekanpur
Tekanpur is a town in Madhya Pradesh, India, best known for hosting the Border Security Force’s main training academy.
- 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61b4875ac8190a9c184c075b5db16 |
completed | April 20, 2026, 12:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a075ef8fd0c819090ddc1e1dba8477c |
completed | May 15, 2026, 5:59 p.m. |
| NEDg | Description generation | batch_6a075fbe27a081909c6869abfdfb6079 |
completed | May 15, 2026, 6:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07608dfcb481908833ecb65c10b96b |
completed | May 15, 2026, 6:06 p.m. |
Created at: April 10, 2026, 1:36 p.m.