Triple
T36635342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rupert Sent Leger |
E904444
|
entity |
| Predicate | languageOfNarrationContext |
P52200
|
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: [Rupert Sent Leger, languageOfNarrationContext, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfNarrationContext Context triple: [Rupert Sent Leger, languageOfNarrationContext, English]
-
A.
languageOfPrimaryNarrations
Indicates the language in which the main or primary narrations are expressed or conveyed.
-
B.
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).
-
C.
languageModality
Indicates the mode or form in which a language is expressed or perceived (e.g., spoken, signed, written, or tactile).
-
D.
originalLanguageContext
Indicates the language in which something was first created or expressed, providing the original linguistic context for its content or meaning.
-
E.
languageUsedInDepiction
Indicates that a particular language is used within a depiction, such as in its text, dialogue, or other linguistic content.
- 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_69f76e6c63e48190b1d0c3a79a6c7406 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a013e23698c81909a32d371b6f158d0 |
completed | May 11, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_6a013db04b108190985897aa6e95b4ec |
completed | May 11, 2026, 2:23 a.m. |
Created at: May 3, 2026, 4:11 p.m.