Triple
T38526218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Love You Wall |
E923234
|
entity |
| Predicate | languageExample |
P60517
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [I Love You Wall, languageExample, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageExample Context triple: [I Love You Wall, languageExample, French]
-
A.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
B.
languageName
Indicates the specific name assigned to a language in the relationship.
-
C.
languageUsedAs
chosen
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
D.
languageDesigned
Indicates that one entity created or developed the language used or associated with another entity.
-
E.
languageSpecifies
Indicates that one entity defines or constrains the syntax, semantics, or usage rules that govern how another language or linguistic system is expressed or interpreted.
- 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_69f76ea8f6348190a5c03fb6292bbee3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a02206665508190ae2324d253ccf377 |
completed | May 11, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_6a021fdc6e54819082847bedd97a680b |
completed | May 11, 2026, 6:28 p.m. |
Created at: May 3, 2026, 4:32 p.m.