Triple
T29186547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rasa (aesthetic flavor) |
E739882
|
entity |
| Predicate | includesRasa |
P1393
|
FINISHED |
| Object | Shringara (erotic / love) |
—
|
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: Shringara (erotic / love) | Statement: [Rasa (aesthetic flavor), includesRasa, Shringara (erotic / love)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesRasa Context triple: [Rasa (aesthetic flavor), includesRasa, Shringara (erotic / love)]
-
A.
includes
chosen
Indicates that one entity contains, encompasses, or has another entity as a part, member, or subset.
-
B.
reasoningIncludes
Indicates that a reasoning process or argument explicitly incorporates or makes use of the referenced element as one of its components or steps.
-
C.
includesSpeech
Indicates that one entity contains, features, or incorporates spoken language or dialogue as part of its content or behavior.
-
D.
aimedToInclude
Indicates that one entity intentionally sought to have another entity or element contained or incorporated within it.
-
E.
includesSee
Indicates that one entity’s scope, content, or experience contains or encompasses the act of seeing or visual perception involving another entity.
- 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_69f07cb74c2c8190ad396487fcb4fde6 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69ff7dcedab08190a719a707d03306e2 |
completed | May 9, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69ff7d0119348190ad462554e81190fe |
completed | May 9, 2026, 6:29 p.m. |
Created at: April 28, 2026, noon