Triple
T12828733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyari Town |
E306728
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object | Daryaabad |
E1006707
|
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: Daryaabad | Statement: [Lyari Town, hasNeighbourhood, Daryaabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daryaabad Context triple: [Lyari Town, hasNeighbourhood, Daryaabad]
-
A.
Daryabad
chosen
Daryabad is a residential neighborhood located within Lyari Town in Karachi, Pakistan, known for its dense urban setting and vibrant local community.
-
B.
Nazarabad
Nazarabad is a city in Iran that serves as an important urban center within Alborz Province.
-
C.
Astarabad
Astarabad is the historical name of the city now known as Gorgan in northeastern Iran, once an important regional center near the Caspian Sea.
-
D.
Nowshakh
Nowshakh is a prominent mountain peak in the Hindu Kush range, located on the border between Afghanistan and Pakistan.
-
E.
Shemakhan
Shemakhan is a fictional realm best known as the exotic homeland of the seductive Queen of Shemakha in Russian literature and opera.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96faf9ae481908265e198f917d1e6 |
completed | April 10, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a5466d988190ae0df2f4058287a1 |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:34 p.m.