Triple
T1749621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corporal Henderson |
E38408
|
entity |
| Predicate | languageOfFilm |
P4185
|
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: [Corporal Henderson, languageOfFilm, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfFilm Context triple: [Corporal Henderson, languageOfFilm, English]
-
A.
areSpokenIn
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
B.
originalTitleLanguage
Indicates the language in which a work’s original title was written or expressed.
-
C.
languageOfReleases
Indicates the language in which the releases associated with an entity are produced or published.
-
D.
languageCodeISO639-1
Indicates that the subject entity is associated with the specified two-letter ISO 639-1 language code.
-
E.
isLanguageOf
chosen
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab630e7d008190a8c673665d9672bb |
completed | March 6, 2026, 11:28 p.m. |
| PD | Predicate disambiguation | batch_69aa61c5a18481909bc49e0c54d64314 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.