Triple
T24459417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Llewellyn Rhys Prize |
E616775
|
entity |
| Predicate | awardedForWorkLanguage |
P118239
|
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: [John Llewellyn Rhys Prize, awardedForWorkLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardedForWorkLanguage Context triple: [John Llewellyn Rhys Prize, awardedForWorkLanguage, English]
-
A.
hasWorkedInLanguage
Indicates that an entity has performed work or professional activities using a particular language.
-
B.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
C.
lenguaDeTrabajo
Indicates that something functions as a working language used for communication in a specific context or setting.
-
D.
primaryLanguageInWork
chosen
Indicates that a specified language is the main or predominant language used within a particular work (such as a book, film, or document).
-
E.
languageOfUnderlyingWork
Indicates the language in which the original or underlying work (from which a derived or related work stems) 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_69e2d7ef9fe08190a0613908758b4e86 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f298c8d854819091f1d92eef02b1b1 |
completed | April 29, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69f287d3237c819099559c00f83131d8 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:19 a.m.