Triple
T822339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chenab River |
E17776
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object |
Sialkot
Sialkot is a major industrial city in Pakistan’s Punjab province, renowned globally for its production of sports goods and surgical instruments.
|
E101948
|
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: Sialkot | Statement: [Chenab River, passesNear, Sialkot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sialkot Context triple: [Chenab River, passesNear, Sialkot]
-
A.
Haripur
Haripur is a city in northern Pakistan known as an administrative and commercial center in the Hazara region of Khyber Pakhtunkhwa.
-
B.
Gujranwala
Gujranwala is a major industrial city in Pakistan’s Punjab province, known for its manufacturing base and historical significance in the region.
-
C.
Jhang
Jhang is a historic city in the Punjab province of Pakistan, known for its cultural heritage and as the birthplace of several notable figures.
-
D.
Nawabshah
Nawabshah is a major city in Pakistan known as an important commercial and agricultural center in the Sindh province.
-
E.
Multan
Multan is a historic city in southern Punjab, Pakistan, renowned as a major cultural, commercial, and Sufi spiritual center with a legacy spanning over two millennia.
- 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: Sialkot Triple: [Chenab River, passesNear, Sialkot]
Generated description
Sialkot is a major industrial city in Pakistan’s Punjab province, renowned globally for its production of sports goods and surgical instruments.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sialkot Target entity description: Sialkot is a major industrial city in Pakistan’s Punjab province, renowned globally for its production of sports goods and surgical instruments.
-
A.
Haripur
Haripur is a city in northern Pakistan known as an administrative and commercial center in the Hazara region of Khyber Pakhtunkhwa.
-
B.
Gujranwala
Gujranwala is a major industrial city in Pakistan’s Punjab province, known for its manufacturing base and historical significance in the region.
-
C.
Jhang
Jhang is a historic city in the Punjab province of Pakistan, known for its cultural heritage and as the birthplace of several notable figures.
-
D.
Nawabshah
Nawabshah is a major city in Pakistan known as an important commercial and agricultural center in the Sindh province.
-
E.
Multan
Multan is a historic city in southern Punjab, Pakistan, renowned as a major cultural, commercial, and Sufi spiritual center with a legacy spanning over two millennia.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab7c139c8190b6d75661b5138d89 |
completed | March 1, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3b6f0a0819086f8789773f8251e |
completed | March 4, 2026, 3:15 a.m. |
| NEDg | Description generation | batch_69a7a51e0420819092f792ce0b5e69d3 |
completed | March 4, 2026, 3:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7a5b041848190bfa91737c71c217c |
completed | March 4, 2026, 3:23 a.m. |
Created at: March 1, 2026, 7:38 p.m.