Triple
T30461822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nerkonda Paarvai |
E775032
|
entity |
| Predicate | mainCourtroomSetting |
P69181
|
FINISHED |
| Object | Chennai court |
—
|
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: Chennai court | Statement: [Nerkonda Paarvai, mainCourtroomSetting, Chennai court]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCourtroomSetting Context triple: [Nerkonda Paarvai, mainCourtroomSetting, Chennai court]
-
A.
courtroom
Indicates a relationship where a legal proceeding or judicial action takes place within or is associated with a specific courtroom.
-
B.
courtEnvironment
Indicates the environmental conditions, setting, or contextual factors present in or around a court that may influence activities or outcomes there.
-
C.
hasCourtroomsFor
Indicates that an entity provides or contains courtrooms designated for use by another entity or purpose.
-
D.
hasCourtroomScenes
chosen
Indicates that the work contains one or more scenes set in a courtroom or depicting courtroom proceedings.
-
E.
hostCourtOf
Indicates that one entity serves as the venue or location where the court associated with another entity is held or convened.
- 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_69f2249622a48190b1fae2e3e4ee958a |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: April 29, 2026, 8:10 p.m.