Triple
T12190336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal B Area |
E290444
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Yaseenabad
Yaseenabad is a residential neighborhood located within the Federal B Area of Karachi, Pakistan.
|
E976753
|
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: Yaseenabad | Statement: [Federal B Area, hasPart, Yaseenabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yaseenabad Context triple: [Federal B Area, hasPart, Yaseenabad]
-
A.
Shikohabad
Shikohabad is a city in the Indian state of Uttar Pradesh, known for its location along major road and rail routes and its role as a regional commercial center.
-
B.
Asifabad
Asifabad is a town in the Kumuram Bheem Asifabad district of Telangana, India, known for its forests, tribal communities, and proximity to the Maharashtra border.
-
C.
Murtazabad
Murtazabad is a village in the Lower Hunza region of northern Pakistan, known for its mountainous landscape and proximity to the Hunza River.
-
D.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
-
E.
Osmanabad
Osmanabad is a city in the Indian state of Maharashtra, known as an important urban and administrative center in the Marathwada region.
- 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: Yaseenabad Triple: [Federal B Area, hasPart, Yaseenabad]
Generated description
Yaseenabad is a residential neighborhood located within the Federal B Area of Karachi, Pakistan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yaseenabad Target entity description: Yaseenabad is a residential neighborhood located within the Federal B Area of Karachi, Pakistan.
-
A.
Shikohabad
Shikohabad is a city in the Indian state of Uttar Pradesh, known for its location along major road and rail routes and its role as a regional commercial center.
-
B.
Asifabad
Asifabad is a town in the Kumuram Bheem Asifabad district of Telangana, India, known for its forests, tribal communities, and proximity to the Maharashtra border.
-
C.
Murtazabad
Murtazabad is a village in the Lower Hunza region of northern Pakistan, known for its mountainous landscape and proximity to the Hunza River.
-
D.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
-
E.
Osmanabad
Osmanabad is a city in the Indian state of Maharashtra, known as an important urban and administrative center in the Marathwada region.
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c5340248190b79379423f3a3ca1 |
completed | April 10, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a88b3b08190aef789b1965bbe80 |
completed | May 2, 2026, 4:47 p.m. |
| NEDg | Description generation | batch_69f62c54e6b08190bdae0ec35cc1c48d |
completed | May 2, 2026, 4:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f62d13eca08190a7181f8427e37066 |
completed | May 2, 2026, 4:57 p.m. |
Created at: April 8, 2026, 9:50 p.m.