Triple
T28492431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Tale of Two Cities (film score) |
E721011
|
entity |
| Predicate | narrativeSettingOfSourceWork |
P45019
|
FINISHED |
| Object | French Revolution |
—
|
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: French Revolution | Statement: [A Tale of Two Cities (film score), narrativeSettingOfSourceWork, French Revolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narrativeSettingOfSourceWork Context triple: [A Tale of Two Cities (film score), narrativeSettingOfSourceWork, French Revolution]
-
A.
narrativeSettingOfWork
chosen
Indicates that a particular place, time, or context serves as the narrative setting in which a work’s story or events occur.
-
B.
workOfFictionSetting
Indicates that a work of fiction is set in, or primarily takes place within, a particular location, time, or environment.
-
C.
placeOfSetting
Indicates the location or environment where an event, scene, or situation takes place.
-
D.
locationOfNarrative
Indicates the place or setting where the events or story described in the narrative occur.
-
E.
hasLiterarySetting
Indicates that a literary work is set in, or primarily takes place within, a particular location or environment.
- 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_69f01a5afdac8190ac6e72d5c100bd58 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f6b2a65c7c8190ac40f1466ceadefc |
completed | May 3, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f6b14d7d508190bc7d4c89dfba4a32 |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 28, 2026, 3:02 a.m.