Triple
T36687279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benkiya Bale |
E905851
|
entity |
| Predicate | hasAuthorEthnicOrRegionalIdentity |
P159764
|
FINISHED |
| Object | Kannadiga |
—
|
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: Kannadiga | Statement: [Benkiya Bale, hasAuthorEthnicOrRegionalIdentity, Kannadiga]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorEthnicOrRegionalIdentity Context triple: [Benkiya Bale, hasAuthorEthnicOrRegionalIdentity, Kannadiga]
-
A.
hasAuthorCulturalIdentity
Indicates that an author is associated with a particular cultural identity or background.
-
B.
creatorEthnicityOfAuthor
Indicates that the specified ethnicity is attributed to the creator who is the author of the referenced work or entity.
-
C.
hasEthnicOrRegionalOrigin
chosen
Indicates that an entity originates from, or is associated with, a particular ethnic group or geographic region.
-
D.
hasBiographicalSubjectEthnicity
Indicates that the biographical subject is associated with a specific ethnicity.
-
E.
hasEthnicityInFiction
Indicates that a fictional character or entity is portrayed as having a particular ethnicity within a narrative or fictional context.
- 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_69f76e70d2448190bdd3ce781ba971c5 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd8e5f7c4c8190ab8e2f2a7bb1bd79 |
completed | May 8, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69fd8d8a16f08190b9e880901bfa44fe |
completed | May 8, 2026, 7:15 a.m. |
Created at: May 3, 2026, 4:12 p.m.