Triple
T32117348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaffer Conservatory |
E820273
|
entity |
| Predicate | hasStudentFictional |
P198418
|
FINISHED |
| Object | Andrew Neiman |
—
|
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: Andrew Neiman | Statement: [Shaffer Conservatory, hasStudentFictional, Andrew Neiman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStudentFictional Context triple: [Shaffer Conservatory, hasStudentFictional, Andrew Neiman]
-
A.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
-
B.
hasFictionalContent
Indicates that something contains or includes material that is imaginary, invented, or not intended to represent real events or facts.
-
C.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
D.
hasChildInFiction
Indicates that a fictional work or character includes another character as their child within the fictional narrative.
-
E.
hasFictionalDocument
Indicates that one entity possesses, is associated with, or includes a document that is fictional or exists only within an imagined or narrative context.
- 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_69fee25dbca481909e6f1c255122b3a8 |
completed | May 9, 2026, 7:29 a.m. |
| PD | Predicate disambiguation | batch_69fee1c8915c8190b08b63e42881f1a9 |
completed | May 9, 2026, 7:27 a.m. |
| PDg | Predicate description generation | batch_69fee25c7c548190a2c6e50074a33da4 |
completed | May 9, 2026, 7:29 a.m. |
Created at: May 1, 2026, 12:28 a.m.