Triple
T5044651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Only Came to Use the Phone |
E113631
|
entity |
| Predicate | collectionOriginalLanguage |
P60993
|
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: [I Only Came to Use the Phone, collectionOriginalLanguage, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: collectionOriginalLanguage Context triple: [I Only Came to Use the Phone, collectionOriginalLanguage, Spanish]
-
A.
originalLanguageCountry
Indicates the country where a work’s original language is primarily spoken or officially used.
-
B.
originalLanguageOfFilmOrTVShow
Indicates the language in which a film or TV show was originally produced and released.
-
C.
originalNameLanguage
Indicates that the specified language is the language in which an entity’s original or primary name was expressed.
-
D.
hasLanguageOfOrigin
Indicates that one entity has its origin or source in the language specified by another entity.
-
E.
originalLanguageSupport
Indicates that one entity provides or maintains functionality, content, or interaction in the original language of another entity.
- F. None of above. chosen
Provenance (4 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_69bd44391fc48190a311ce9c826c209b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73fd81788190b7799f519277119a |
completed | March 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69bd71529d608190a53470ba6c14bb1d |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd73617f348190b2fa68a0ef4fc7b1 |
completed | March 20, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:37 p.m.