Triple
T11342637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Templar is Christian |
E268637
|
entity |
| Predicate | literaryWorkGenreContext |
P22130
|
FINISHED |
| Object | Enlightenment 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: Enlightenment drama | Statement: [The Templar is Christian, literaryWorkGenreContext, Enlightenment drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryWorkGenreContext Context triple: [The Templar is Christian, literaryWorkGenreContext, Enlightenment drama]
-
A.
literaryGenreOfWork
chosen
Indicates that a work belongs to or is classified under a particular literary genre.
-
B.
literaryGenreOfSourceWork
Indicates that a work belongs to, or is characterized by, a particular literary genre.
-
C.
hasLiteraryContext
Indicates that something is associated with, situated within, or explained by a particular literary context (such as a work, genre, period, or interpretive framework).
-
D.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
E.
literatureType
Indicates the specific category or genre of literature that characterizes or classifies a given work or text.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d80148e2048190a716b515d78efdd1 |
completed | April 9, 2026, 7:43 p.m. |
| PD | Predicate disambiguation | batch_69d7e6f8aeb4819080476f16a69b2ee3 |
completed | April 9, 2026, 5:50 p.m. |
Created at: April 8, 2026, 9:33 p.m.