Triple
T36647712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bajrangi Bhaijaan |
E904756
|
entity |
| Predicate | childCharacterNationality |
P15237
|
FINISHED |
| Object | Pakistani |
—
|
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: Pakistani | Statement: [Bajrangi Bhaijaan, childCharacterNationality, Pakistani]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: childCharacterNationality Context triple: [Bajrangi Bhaijaan, childCharacterNationality, Pakistani]
-
A.
nationalityInStory
chosen
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
-
B.
userNationality
Indicates that a user has a specific national affiliation or citizenship.
-
C.
targetNationality
Indicates that one entity has the specified nationality as its intended or designated target.
-
D.
ownerNationality
Indicates that the owner of an entity has the specified nationality.
-
E.
nationalityInHumanWorld
Indicates that one entity has the specified national affiliation or citizenship within the context of the human world.
- 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_69f76e6d3a3c81909db73eda9e0516bd |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c477a4d481908f52e55b6688f60c |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:11 p.m.