Triple
T36322583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Lennon in The Hours and Times |
E894375
|
entity |
| Predicate | genreOfFictionalization |
P116833
|
FINISHED |
| Object | biographical drama |
—
|
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: biographical drama | Statement: [John Lennon in The Hours and Times, genreOfFictionalization, biographical drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreOfFictionalization Context triple: [John Lennon in The Hours and Times, genreOfFictionalization, biographical drama]
-
A.
fictionalGenre
Indicates that a work of fiction belongs to or is categorized under a particular narrative genre or style.
-
B.
fictionalType
Indicates that one entity is a fictional or imaginary type or category of the other entity.
-
C.
hasGenreInFiction
chosen
Indicates that a work of fiction belongs to or is categorized under a specific literary genre.
-
D.
literaryGenreOfWork
Indicates that a work belongs to or is classified under a particular literary genre.
-
E.
literaryGenreOfSourceWork
Indicates that a work belongs to, or is characterized by, a particular literary genre.
- 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_69f76e4d1a788190a6ab6ccca28547a7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7ba6d06f48190a71b5a2f19e2232f |
completed | May 3, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a4aad48190a62e41c5e39339d9 |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:09 p.m.