Triple
T2727953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malir District |
E60238
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bin Qasim Town
Bin Qasim Town is a residential and industrial locality in the eastern part of Karachi, Pakistan, known for its proximity to the Port Qasim industrial area.
|
E292895
|
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: Bin Qasim Town | Statement: [Malir District, contains, Bin Qasim Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bin Qasim Town Context triple: [Malir District, contains, Bin Qasim Town]
-
A.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
-
B.
Nasirabad
Nasirabad is a town and administrative area located in the Balochistan region of present-day Pakistan.
-
C.
Farah city
Farah city is the capital of Farah Province in southwestern Afghanistan, serving as a regional center for trade and agriculture.
-
D.
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.
-
E.
Bannu
Bannu is a historic city in northwestern Pakistan known as a regional commercial and cultural center in the Khyber Pakhtunkhwa province.
- 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: Bin Qasim Town Triple: [Malir District, contains, Bin Qasim Town]
Generated description
Bin Qasim Town is a residential and industrial locality in the eastern part of Karachi, Pakistan, known for its proximity to the Port Qasim industrial area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bin Qasim Town Target entity description: Bin Qasim Town is a residential and industrial locality in the eastern part of Karachi, Pakistan, known for its proximity to the Port Qasim industrial area.
-
A.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
-
B.
Nasirabad
Nasirabad is a town and administrative area located in the Balochistan region of present-day Pakistan.
-
C.
Farah city
Farah city is the capital of Farah Province in southwestern Afghanistan, serving as a regional center for trade and agriculture.
-
D.
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.
-
E.
Bannu
Bannu is a historic city in northwestern Pakistan known as a regional commercial and cultural center in the Khyber Pakhtunkhwa province.
- 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_69ab4b75cd908190b691ef0d1801acda |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdacffa6481909df37335e8fdd595 |
completed | March 7, 2026, 7:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afb69aa8b081909c57e8a7f64d0913 |
completed | March 10, 2026, 6:13 a.m. |
| NEDg | Description generation | batch_69afb717260c8190a93641152f163879 |
completed | March 10, 2026, 6:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb781327c819090c42c461d17762e |
completed | March 10, 2026, 6:17 a.m. |
Created at: March 6, 2026, 9:56 p.m.