Triple
T11781124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karachi Division |
E280147
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Hub District |
E282146
|
NE FINISHED |
How this triple was built (2 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: Hub District | Statement: [Karachi Division, borderedBy, Hub District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hub District Context triple: [Karachi Division, borderedBy, Hub District]
-
A.
Hub District
chosen
Hub District is an administrative district in Pakistan’s Balochistan province, located adjacent to the western edge of Karachi.
-
B.
Dynamo District
Dynamo District is a Moscow neighborhood known for its major sports and entertainment facilities, including the VTB Arena complex.
-
C.
Co Do District
Co Do District is a rural administrative district of Cần Thơ city in Vietnam’s Mekong Delta region.
-
D.
Tank District
Tank District is an administrative district in the Khyber Pakhtunkhwa province of Pakistan, known for its arid terrain and location near the country's western border regions.
-
E.
Harborland district
Harborland district is a popular waterfront shopping and entertainment area in Kobe, Japan, known for its modern malls, restaurants, and scenic harbor views.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab01d2688190ad8ed6bda487eaa5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a5623f708190a18aea570577a3f6 |
completed | April 10, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f090c828f0819097662c048542b5da |
completed | April 28, 2026, 10:49 a.m. |
Created at: April 8, 2026, 9:42 p.m.