Triple
T32117364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaffer Conservatory |
E820273
|
entity |
| Predicate | genreOfFictionalInstitution |
P71478
|
FINISHED |
| Object | drama film setting |
—
|
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: drama film setting | Statement: [Shaffer Conservatory, genreOfFictionalInstitution, drama film setting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreOfFictionalInstitution Context triple: [Shaffer Conservatory, genreOfFictionalInstitution, drama film setting]
-
A.
fictionalEntityType
Indicates that the subject is classified as a particular type or category of fictional entity within a narrative or imaginary context.
-
B.
hasFictionalEstablishmentType
chosen
Indicates that an establishment is associated with a particular type or category of fictional setting or institution.
-
C.
bodyTypeInFiction
Indicates how a particular body type is portrayed, characterized, or represented within fictional works.
-
D.
fictionalType
Indicates that one entity is a fictional or imaginary type or category of the other entity.
-
E.
fictionalGenre
Indicates that a work of fiction belongs to or is categorized under a particular narrative genre or style.
- 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_69f3490209c881908ec0241476715f15 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: May 1, 2026, 12:28 a.m.