Triple
T9012646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brilliant Lady |
E215511
|
entity |
| Predicate | languageOnboard |
P82602
|
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: [Brilliant Lady, languageOnboard, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOnboard Context triple: [Brilliant Lady, languageOnboard, English]
-
A.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
B.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
C.
navigationLanguage
chosen
Indicates the language used for navigation-related content, such as menus, directions, or interface controls.
-
D.
languageLabel
Indicates the human-readable name or label of a language associated with an entity or resource.
-
E.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69f96980819093bfc49d48570c65 |
completed | April 1, 2026, 12:42 a.m. |
| PD | Predicate disambiguation | batch_69cc5edf84408190aa5f57cb8bfd00e1 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:06 p.m.