Triple
T33687739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tangere |
E863086
|
entity |
| Predicate | famousUsageLanguage |
P58450
|
FINISHED |
| Object | Latin Vulgate |
—
|
NE NERFINISHED |
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: Latin Vulgate | Statement: [Tangere, famousUsageLanguage, Latin Vulgate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: famousUsageLanguage Context triple: [Tangere, famousUsageLanguage, Latin Vulgate]
-
A.
languagesUsed
chosen
Indicates that one entity uses, employs, or is expressed in one or more languages associated with the other entity.
-
B.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
C.
alsoUsedLanguageInProgramming
Indicates that an entity used an additional programming language, beyond a primary one, in the context of programming.
-
D.
notableMemberLanguage
Indicates that the language is notably associated with or used by a prominent member of the referenced group or entity.
-
E.
hasTypicalPerformanceLanguage
Indicates that an entity is commonly or characteristically expressed, implemented, or described using a particular programming or specification language.
- 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_69f3498662b48190904442c39df84fb7 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6ffbad8848190867c2988c0ceb84f |
completed | May 3, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69f6fc5740fc81909774a4f65201a3ff |
completed | May 3, 2026, 7:42 a.m. |
Created at: May 1, 2026, 1:43 a.m.