Triple
T18631489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Jerónimo |
E455427
|
entity |
| Predicate | hasLanguageOfCrew |
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: [San Jerónimo, hasLanguageOfCrew, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfCrew Context triple: [San Jerónimo, hasLanguageOfCrew, Spanish]
-
A.
workLanguageOfTitle
Indicates the language in which a specific work or title is expressed or written.
-
B.
hasCrewNationality
Indicates that the members of a crew possess a specified nationality.
-
C.
filmedInLanguage
Indicates that a film or video work was originally recorded using a particular spoken or signed language.
-
D.
areSpokenIn
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
E.
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).
- 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fc4c5648190b771e9b080e98c15 |
completed | April 19, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69e478d4a7948190a4bb9223bb5dddfc |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:46 a.m.