Triple
T19043642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gurdwara Khadur Sahib |
E466072
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Khadur Sahib town
Khadur Sahib town is a historic settlement in Punjab, India, closely associated with Sikh history and the residence of Guru Angad Dev Ji.
|
E1355745
|
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: Khadur Sahib town | Statement: [Gurdwara Khadur Sahib, namedAfter, Khadur Sahib town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Khadur Sahib town Context triple: [Gurdwara Khadur Sahib, namedAfter, Khadur Sahib town]
-
A.
Barhi town
Barhi town is a small urban settlement in India known for its proximity to an industrial area that supports local economic activity.
-
B.
Mithankot
Mithankot is a town in southern Punjab, Pakistan, situated near Kot Mithan along the Indus River.
-
C.
Rajanpur
Rajanpur is a city in Pakistan known as an administrative and commercial center in the southern part of Punjab province.
-
D.
Kulu town
Kulu town is a small urban settlement in central Turkey that serves as the local administrative and service hub for the surrounding Kulu district.
-
E.
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.
- 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: Khadur Sahib town Triple: [Gurdwara Khadur Sahib, namedAfter, Khadur Sahib town]
Generated description
Khadur Sahib town is a historic settlement in Punjab, India, closely associated with Sikh history and the residence of Guru Angad Dev Ji.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Khadur Sahib town Target entity description: Khadur Sahib town is a historic settlement in Punjab, India, closely associated with Sikh history and the residence of Guru Angad Dev Ji.
-
A.
Barhi town
Barhi town is a small urban settlement in India known for its proximity to an industrial area that supports local economic activity.
-
B.
Mithankot
Mithankot is a town in southern Punjab, Pakistan, situated near Kot Mithan along the Indus River.
-
C.
Rajanpur
Rajanpur is a city in Pakistan known as an administrative and commercial center in the southern part of Punjab province.
-
D.
Kulu town
Kulu town is a small urban settlement in central Turkey that serves as the local administrative and service hub for the surrounding Kulu district.
-
E.
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.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d802a75c8190a4ce45e5fbffc1b7 |
completed | April 20, 2026, 7:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05c54f0fbc8190aa1c8d0cae900aec |
completed | May 14, 2026, 12:51 p.m. |
| NEDg | Description generation | batch_6a05c7c2a60c819081cd60e47efd84c4 |
completed | May 14, 2026, 1:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05c842970481909deba4dd02002bad |
completed | May 14, 2026, 1:04 p.m. |
Created at: April 10, 2026, 12:03 p.m.