Triple
T29883742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Notre Dame de Paris (Spanish cast version) |
E758952
|
entity |
| Predicate | castLanguage |
P52200
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [Notre Dame de Paris (Spanish cast version), castLanguage, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: castLanguage Context triple: [Notre Dame de Paris (Spanish cast version), castLanguage, Spanish]
-
A.
filmedInLanguage
Indicates that a film or video work was originally recorded using a particular spoken or signed language.
-
B.
areSpokenIn
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
C.
primaryFilmingLanguage
Indicates the main language in which a film or audiovisual work was originally filmed or recorded.
-
D.
languageSpokenOnScreen
chosen
Indicates that a particular language is used in spoken dialogue or audible communication within an on-screen work (such as a film, show, or video).
-
E.
basedInFilmLanguage
Indicates that something is created, presented, or expressed using the language employed in a particular 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_69f2245de2f48190a481404896b56254 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f676fb3b9c819097dcd5920e0cd09e |
completed | May 2, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69f66ac32b60819092290b2de35988d3 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:59 p.m.