Triple
T5086839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prabhakara school |
E114657
|
entity |
| Predicate | languageTheory |
P19441
|
FINISHED |
| Object | anvitābhidhāna (connected denotation) |
—
|
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: anvitābhidhāna (connected denotation) | Statement: [Prabhakara school, languageTheory, anvitābhidhāna (connected denotation)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageTheory Context triple: [Prabhakara school, languageTheory, anvitābhidhāna (connected denotation)]
-
A.
languageParadigm
Indicates a relationship where a programming language follows, supports, or is categorized under a particular programming paradigm.
-
B.
inLanguageTheory
chosen
Indicates that something is analyzed, described, or formulated within a particular linguistic or formal language theory.
-
C.
languageFeature
Indicates that one entity is a characteristic, property, or capability of a language associated with the other entity.
-
D.
programmingLanguage
Indicates that one entity is a programming language used to create, control, or interact with the other entity.
-
E.
languageOfCode
Indicates that a programming code artifact is written in, or uses, a particular programming language.
- 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_69bd443e941881908eb4e8c685b6f656 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd751fb6dc8190be674c92a17e8c0e |
completed | March 20, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69bd7159adc881909effd4382c395c66 |
completed | March 20, 2026, 4:10 p.m. |
Created at: March 20, 2026, 1:40 p.m.