Triple
T7660412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meghe Dhaka Tara |
E173488
|
entity |
| Predicate | hasFilmLocationSetting |
P52439
|
FINISHED |
| Object | refugee colony |
—
|
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: refugee colony | Statement: [Meghe Dhaka Tara, hasFilmLocationSetting, refugee colony]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmLocationSetting Context triple: [Meghe Dhaka Tara, hasFilmLocationSetting, refugee colony]
-
A.
filmLocationFor
Indicates a relationship where a specific place serves as the filming location for a particular film or production.
-
B.
filmSetting
chosen
Indicates the place, time, or environment in which the events of a film are set or take place.
-
C.
hasShootingLocation
Indicates that an audiovisual work was filmed or recorded at a particular location.
-
D.
filmingLocationContext
Indicates the contextual relationship specifying where the filming of an event, scene, or production took place.
-
E.
meetsInCamera
Indicates that two or more entities are physically present together in the same camera frame or shot at the same time.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7061cbc3c8190a917dd7e71214182 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015dd8fc8190bc5f52a12bd46209 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 3:59 p.m.