Triple
T27823840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chander Pahar |
E702891
|
entity |
| Predicate | hasFilmAdaptationLanguage |
P121206
|
FINISHED |
| Object | Bengali |
—
|
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: Bengali | Statement: [Chander Pahar, hasFilmAdaptationLanguage, Bengali]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmAdaptationLanguage Context triple: [Chander Pahar, hasFilmAdaptationLanguage, Bengali]
-
A.
filmAdaptationTitleLanguage
Indicates the language in which the title of a film adaptation is expressed.
-
B.
adaptedInLanguage
chosen
Indicates that a work or content has been modified or translated so it can be presented or understood in a specified language.
-
C.
hasFilmAdaptationTitle
Indicates that a creative work has a film adaptation whose title is given by the related value.
-
D.
languageOfMostAdaptations
Indicates the language in which the greatest number of adaptations of a given work or entity have been produced.
-
E.
hasPreviousFilmAdaptation
Indicates that a work has been adapted into a film before the current or referenced adaptation.
- 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_69ef840ad1e88190b5bff2d1ddec8700 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69fbc36ce1f88190a7fa1656b714e107 |
completed | May 6, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69fbbd13595c81908719f52c3d37a7e8 |
completed | May 6, 2026, 10:13 p.m. |
Created at: April 27, 2026, 5:50 p.m.