Triple
T23602066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Padmabati |
E582784
|
entity |
| Predicate | associatedWithRegionInFiction |
P152884
|
FINISHED |
| Object | Bengal |
—
|
NE NERFINISHED |
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: Bengal | Statement: [Padmabati, associatedWithRegionInFiction, Bengal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithRegionInFiction Context triple: [Padmabati, associatedWithRegionInFiction, Bengal]
-
A.
belongsToFictionalRegion
Indicates that an entity is located within, associated with, or under the jurisdiction of a fictional or imaginary geographic region.
-
B.
associatedWithCountryInFiction
Indicates a fictional relationship in which an entity is linked or connected to a particular country within a fictional context or narrative.
-
C.
associatedWithCaseInFiction
Indicates that an entity is connected to, involved in, or relevant to a particular case or investigation within a fictional context.
-
D.
belongsToFictionalContinent
Indicates that something is located on, associated with, or a part of a specific fictional continent within an imagined world.
-
E.
setInFictionalizedRegionOf
Indicates that an event or narrative is located within a region that is a fictionalized or altered version of a real-world place.
- F. None of above. chosen
Provenance (4 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_69e248faa2788190abb1581742daa6aa |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b0947bd8819097e26643dc257dc0 |
completed | April 29, 2026, 7:17 a.m. |
| PD | Predicate disambiguation | batch_69f118c96a0081908a8ac98ef7e7e60c |
completed | April 28, 2026, 8:30 p.m. |
| PDg | Predicate description generation | batch_69f121cc494081908c987adfcde89b0e |
completed | April 28, 2026, 9:08 p.m. |
Created at: April 17, 2026, 6:43 p.m.