Triple
T32117362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaffer Conservatory |
E820273
|
entity |
| Predicate | hasNotableTeacherFictional |
P203035
|
FINISHED |
| Object | Terence Fletcher |
—
|
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: Terence Fletcher | Statement: [Shaffer Conservatory, hasNotableTeacherFictional, Terence Fletcher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableTeacherFictional Context triple: [Shaffer Conservatory, hasNotableTeacherFictional, Terence Fletcher]
-
A.
hasStudentFictional
Indicates that an entity has, is associated with, or is characterized by a student who is fictional rather than real.
-
B.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
C.
hasNotableWriter
Indicates that an entity is associated with a writer who is recognized as significant or distinguished in some notable way.
-
D.
hasFictionalAuthorOccupation
Indicates that a fictional author character holds or is associated with a particular occupation or job.
-
E.
notableFictionalFeature
Indicates that an entity is distinguished by a specific fictional characteristic, trait, or element it is known for.
- F. None of above. chosen
Provenance (4 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_6a0117b6539c8190be7d231e891fe546 |
completed | May 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_6a011762f7ec8190afc884b92419d33f |
completed | May 10, 2026, 11:40 p.m. |
| PDg | Predicate description generation | batch_6a0117b4eec88190a761ac126f5cc8e2 |
completed | May 10, 2026, 11:41 p.m. |
Created at: May 1, 2026, 12:28 a.m.