Triple
T6486208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Punjab, Pakistan |
E146516
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Sargodha
Sargodha is a major city in central Pakistan known for its air force base and extensive citrus (particularly kinnow) production.
|
E606500
|
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: Sargodha | Statement: [Punjab, Pakistan, containsCity, Sargodha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sargodha Context triple: [Punjab, Pakistan, containsCity, Sargodha]
-
A.
Chakwal
Chakwal is a city in Pakistan’s Punjab province, known as a regional administrative and commercial center in the Potohar Plateau area.
-
B.
Bahawalpur
Bahawalpur is a historic city in southern Punjab, Pakistan, known for its former princely state status, grand palaces, and proximity to the Cholistan Desert.
-
C.
Khanewal
Khanewal is a prominent city in Pakistan’s Punjab province, known as an important railway junction and agricultural trade center.
-
D.
Bahawalnagar
Bahawalnagar is a prominent city in Pakistan’s Punjab province, known as an agricultural and commercial hub near the border with India.
-
E.
Faisalabad
Faisalabad is a major industrial city in Pakistan’s Punjab province, known especially for its large textile industry and role as a commercial hub.
- 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: Sargodha Triple: [Punjab, Pakistan, containsCity, Sargodha]
Generated description
Sargodha is a major city in central Pakistan known for its air force base and extensive citrus (particularly kinnow) production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sargodha Target entity description: Sargodha is a major city in central Pakistan known for its air force base and extensive citrus (particularly kinnow) production.
-
A.
Chakwal
Chakwal is a city in Pakistan’s Punjab province, known as a regional administrative and commercial center in the Potohar Plateau area.
-
B.
Bahawalpur
Bahawalpur is a historic city in southern Punjab, Pakistan, known for its former princely state status, grand palaces, and proximity to the Cholistan Desert.
-
C.
Khanewal
Khanewal is a prominent city in Pakistan’s Punjab province, known as an important railway junction and agricultural trade center.
-
D.
Bahawalnagar
Bahawalnagar is a prominent city in Pakistan’s Punjab province, known as an agricultural and commercial hub near the border with India.
-
E.
Faisalabad
Faisalabad is a major industrial city in Pakistan’s Punjab province, known especially for its large textile industry and role as a commercial hub.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a706d4c8190b7a3cc8855abcecb |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e40c193c8190b4d7acd4530121f0 |
completed | March 27, 2026, 8:09 p.m. |
| NEDg | Description generation | batch_69c6e50db2cc8190932c4d44257acb83 |
completed | March 27, 2026, 8:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6e5eacefc819092d0e9f79d90c4a6 |
completed | March 27, 2026, 8:17 p.m. |
Created at: March 22, 2026, 4:52 p.m.