Triple
T20454583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hazrat Shah Jalal Mazar Sharif |
E501741
|
entity |
| Predicate | nearbyUrbanFeature |
P36605
|
FINISHED |
| Object | central Sylhet city |
—
|
LITERAL 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: central Sylhet city | Statement: [Hazrat Shah Jalal Mazar Sharif, nearbyUrbanFeature, central Sylhet city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyUrbanFeature Context triple: [Hazrat Shah Jalal Mazar Sharif, nearbyUrbanFeature, central Sylhet city]
-
A.
nearbyUrbanCenter
chosen
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
infrastructureNearby
Indicates that one entity is located close to another entity that serves as infrastructure (such as roads, utilities, or public facilities).
-
C.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
D.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
E.
nearbyFeature
Indicates that one entity is located close to or in the immediate vicinity of another entity.
- F. None of above.
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_69e0b4ad4940819098cf2ff6413574e5 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68d04ca4081909b428c31d16fca10 |
completed | April 20, 2026, 8:31 p.m. |
| PD | Predicate disambiguation | batch_69e57679eb40819086142df3e39c928e |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 16, 2026, 11:32 a.m.