Triple
T30528580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kadhalikka Neramillai |
E776926
|
entity |
| Predicate | languageRegionPopularity |
P9666
|
FINISHED |
| Object | South Indian cinema |
—
|
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: South Indian cinema | Statement: [Kadhalikka Neramillai, languageRegionPopularity, South Indian cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageRegionPopularity Context triple: [Kadhalikka Neramillai, languageRegionPopularity, South Indian cinema]
-
A.
regionOfMajorLanguage
Indicates the geographic region where a particular language is predominantly spoken or holds major usage.
-
B.
primaryRegionOfPopularity
chosen
Indicates the geographic region where something is most widely used, favored, or popular compared to other regions.
-
C.
namePopularityRegion
Indicates the geographic region or area in which a particular name has a certain level of popularity or usage.
-
D.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
E.
rankedByNumberOfNativeSpeakers
Indicates that entities are ordered or classified according to how many native speakers they have.
- 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_69f2249c11508190ae7e955755ccfb01 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ff1f3f94fc819095955299f50ab4ce |
completed | May 9, 2026, 11:49 a.m. |
| PD | Predicate disambiguation | batch_69ff1ea47748819082f63d9b9d9c3e65 |
completed | May 9, 2026, 11:46 a.m. |
Created at: April 29, 2026, 8:18 p.m.