Triple
T13895845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ayubia |
E334084
|
entity |
| Predicate | nearbyPlace |
P2064
|
FINISHED |
| Object |
Khanaspur
Khanaspur is a small hill station and tourist resort in Pakistan’s Galyat region, known for its cool climate, forested slopes, and scenic mountain views.
|
E1090021
|
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: Khanaspur | Statement: [Ayubia, nearbyPlace, Khanaspur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Khanaspur Context triple: [Ayubia, nearbyPlace, Khanaspur]
-
A.
Khanpur
Khanpur is a prominent town in Rajasthan, India, known as one of the key urban centers of Jhalawar district.
-
B.
Khanpur
Khanpur is a significant urban and commercial center in southern Punjab, Pakistan, known for its agricultural trade and regional connectivity.
-
C.
Rajanpur
Rajanpur is a city in Pakistan known as an administrative and commercial center in the southern part of Punjab province.
-
D.
Sikandarpur
Sikandarpur is a metro station in the Delhi Metro network that serves the Gurugram area and provides an interchange with the Rapid Metro system.
-
E.
Shahpura
Shahpura is a town in Rajasthan, India, historically known as the administrative and cultural center of the former princely Shahpura State.
- 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: Khanaspur Triple: [Ayubia, nearbyPlace, Khanaspur]
Generated description
Khanaspur is a small hill station and tourist resort in Pakistan’s Galyat region, known for its cool climate, forested slopes, and scenic mountain views.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Khanaspur Target entity description: Khanaspur is a small hill station and tourist resort in Pakistan’s Galyat region, known for its cool climate, forested slopes, and scenic mountain views.
-
A.
Khanpur
Khanpur is a prominent town in Rajasthan, India, known as one of the key urban centers of Jhalawar district.
-
B.
Khanpur
Khanpur is a significant urban and commercial center in southern Punjab, Pakistan, known for its agricultural trade and regional connectivity.
-
C.
Rajanpur
Rajanpur is a city in Pakistan known as an administrative and commercial center in the southern part of Punjab province.
-
D.
Sikandarpur
Sikandarpur is a metro station in the Delhi Metro network that serves the Gurugram area and provides an interchange with the Rapid Metro system.
-
E.
Shahpura
Shahpura is a town in Rajasthan, India, historically known as the administrative and cultural center of the former princely Shahpura State.
- 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_69d81c5dd2d48190b7a5fc1e009de936 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de25d72c6c819093bf9c43136839d4 |
completed | April 14, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd323e89948190bb280e93e2058c0a |
completed | May 8, 2026, 12:45 a.m. |
| NEDg | Description generation | batch_69fd33d981408190b017164a04676f4c |
completed | May 8, 2026, 12:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3437f90081908c325f0a36993f57 |
completed | May 8, 2026, 12:54 a.m. |
Created at: April 9, 2026, 10:15 p.m.