Triple
T25446197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kollywood |
E637643
|
entity |
| Predicate | namePatternSimilarTo |
P106359
|
FINISHED |
| Object | Bollywood |
—
|
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: Bollywood | Statement: [Kollywood, namePatternSimilarTo, Bollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namePatternSimilarTo Context triple: [Kollywood, namePatternSimilarTo, Bollywood]
-
A.
namedForSimilarityTo
chosen
Indicates that one entity is given its name because of a perceived resemblance or likeness to another entity.
-
B.
namePattern
Indicates that an entity’s name follows or matches a specified pattern or format.
-
C.
nameContrastsWith
Indicates that one name is deliberately chosen or used to highlight a difference or opposition in meaning, style, or identity relative to another name.
-
D.
hasSimilarityTo
Indicates that one entity shares common characteristics, features, or qualities with another entity to a notable degree.
-
E.
equivalentSurname
Indicates that two entities share the same surname or are considered to have matching surnames for identification or equivalence purposes.
- 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_69e75db7c5048190b8da9cd7eeedb610 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f70346cc8190b7ed6b5a86a508cd |
completed | May 2, 2026, 1:07 p.m. |
| PD | Predicate disambiguation | batch_69f4683b34748190818428489a226124 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 2:02 p.m.