Triple
T27052040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | On Earth as It Is in Heaven |
E684797
|
entity |
| Predicate | associatedWithFilmSetting |
P52439
|
FINISHED |
| Object | 18th-century South America |
—
|
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: 18th-century South America | Statement: [On Earth as It Is in Heaven, associatedWithFilmSetting, 18th-century South America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithFilmSetting Context triple: [On Earth as It Is in Heaven, associatedWithFilmSetting, 18th-century South America]
-
A.
filmSetting
chosen
Indicates the place, time, or environment in which the events of a film are set or take place.
-
B.
associatedWithSetting
Indicates that one entity is connected or linked to a particular context, environment, or setting in which it occurs or is relevant.
-
C.
appliesToFilm
Indicates that something (such as a rule, feature, attribute, or condition) is relevant or applicable to a particular film.
-
D.
associatedWithFilmCharacterType
Indicates that an entity has an association or connection with a particular type or category of film character.
-
E.
associatedWithComposerOfFilm
Indicates a relationship where an entity is connected to the composer who created the musical score for a specific film.
- 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_69ef14829fac8190914bef9ecc3005d7 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69ffab5adf2c819084700c5ea34615bf |
completed | May 9, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69ffaabffa208190b5214ca17cc8a5ea |
completed | May 9, 2026, 9:44 p.m. |
Created at: April 27, 2026, 8:14 a.m.