Triple
T34580490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kate McKay |
E887881
|
entity |
| Predicate | presentInWorkLanguage |
P45484
|
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: [Kate McKay, presentInWorkLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: presentInWorkLanguage Context triple: [Kate McKay, presentInWorkLanguage, English]
-
A.
primaryLanguageInWork
Indicates that a specified language is the main or predominant language used within a particular work (such as a book, film, or document).
-
B.
hasWorkedInLanguage
chosen
Indicates that an entity has performed work or professional activities using a particular language.
-
C.
isCharacterInWorkLanguage
Indicates that a character appears in a work (e.g., book, film, game) in a specific language version or localization.
-
D.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
E.
hasAwardedWorkLanguage
Indicates the language in which an awarded work (such as a prize-winning book, film, or other creation) is expressed.
- 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_69f349d25cbc8190869998de5915886b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fd2839880c819099a7a89783f2270e |
completed | May 8, 2026, 12:03 a.m. |
| PD | Predicate disambiguation | batch_69fd23dc5da48190ae8ba08947d34956 |
completed | May 7, 2026, 11:44 p.m. |
Created at: May 1, 2026, 2:03 a.m.