Triple
T28926153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinema |
E733651
|
entity |
| Predicate | workingNameFor |
P64681
|
FINISHED |
| Object | Yes (during early 1980s reformation) |
—
|
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: Yes (during early 1980s reformation) | Statement: [Cinema, workingNameFor, Yes (during early 1980s reformation)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workingNameFor Context triple: [Cinema, workingNameFor, Yes (during early 1980s reformation)]
-
A.
workingName
chosen
Indicates that one entity serves as a temporary, provisional, or informal name currently used for another entity.
-
B.
namedForWork
Indicates that one entity is named in honor of, or derived from the title of, a particular work (such as a book, film, artwork, or other creative production).
-
C.
namedForWorkOn
Indicates that an entity is named in honor of another entity specifically because of that entity’s work or contributions in a particular field or endeavor.
-
D.
givenNameFor
Indicates that one entity is the personal first name assigned to or used for another entity.
-
E.
occupationalNameFor
Indicates that one entity is the name or label used to denote the occupation or profession of another entity.
- 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_69f05b0b49b08190b8994b339c7980f6 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f65b4f36b481908b3e09dff791edd9 |
completed | May 2, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69f659d02f1c8190831758ac52bb54e4 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 28, 2026, 8:23 a.m.