Triple
T6991936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacBadger |
E162104
|
entity |
| Predicate | languageOfOriginalProduction |
P58177
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [MacBadger, languageOfOriginalProduction, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfOriginalProduction Context triple: [MacBadger, languageOfOriginalProduction, English]
-
A.
originalLanguageOfFilmOrTVShow
chosen
Indicates the language in which a film or TV show was originally produced and released.
-
B.
originalLanguageCountry
Indicates the country where a work’s original language is primarily spoken or officially used.
-
C.
originalTitleLanguage
Indicates the language in which a work’s original title was written or expressed.
-
D.
areSpokenIn
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
E.
originalLanguageTitle
Indicates the title of a work as it appears in its original language of creation or publication.
- 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_69c68856d7808190ab33ee914640281b |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbc1f63c8190837cfd71cf5ed613 |
completed | March 27, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c4a18881908d267137daed828b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:32 p.m.