Triple
T4560411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Konstantin Stanislavski |
E120576
|
entity |
| Predicate | theoryConcept |
P11009
|
FINISHED |
| Object | given circumstances |
—
|
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: given circumstances | Statement: [Konstantin Stanislavski, theoryConcept, given circumstances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: theoryConcept Context triple: [Konstantin Stanislavski, theoryConcept, given circumstances]
-
A.
introducedConcept
Indicates that one entity is responsible for presenting, defining, or bringing a new concept into use or awareness for another entity or context.
-
B.
technologyConcept
Indicates a relationship where one entity represents or embodies a technological idea, principle, or method conceptually associated with another entity.
-
C.
featuredConcept
Indicates that one concept is highlighted or given special prominence relative to others in a particular context.
-
D.
philosophicalConcept
chosen
Indicates that one entity is a philosophical concept that characterizes, explains, or is thematically central to the other entity.
-
E.
studiesConcept
Indicates that an entity engages in learning, examining, or researching a particular concept.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd582b871c8190be0b70c76d639000 |
completed | March 20, 2026, 2:22 p.m. |
| PD | Predicate disambiguation | batch_69bd52254c648190a5144cfe8fa7e409 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:09 p.m.