Triple
T12429914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nazimabad |
E296998
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Gulbahar |
E964450
|
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: Gulbahar | Statement: [Nazimabad, adjacentTo, Gulbahar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gulbahar Context triple: [Nazimabad, adjacentTo, Gulbahar]
-
A.
Gulbahar
chosen
Gulbahar is a residential neighborhood in Karachi, Pakistan, known for its dense urban setting and local commercial activity.
-
B.
Gulbahar
Gulbahar is a town in northeastern Afghanistan situated along the Panjshir River and known as a local commercial and agricultural center.
-
C.
Rukhsana
Rukhsana is a feminine given name, commonly used in South Asian and Muslim cultures, that is a variant of the name Roxana.
-
D.
Gulmancema
Gulmancema is a Gur language spoken primarily by the Gurma people in parts of Burkina Faso and neighboring West African countries.
-
E.
Shabana
Shabana is a prominent Bangladeshi film actress renowned for her extensive and influential career in Bengali cinema.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d7ddc688190bddb242d67fa6e89 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6349b075c8190b77cd51bc45be8ef |
completed | May 2, 2026, 5:30 p.m. |
Created at: April 8, 2026, 9:55 p.m.