Triple
T10356127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hussein Chalayan |
E244001
|
entity |
| Predicate | yearAwardedBritishDesignerOfTheYear |
P93843
|
FINISHED |
| Object | 1999 |
—
|
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: 1999 | Statement: [Hussein Chalayan, yearAwardedBritishDesignerOfTheYear, 1999]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearAwardedBritishDesignerOfTheYear Context triple: [Hussein Chalayan, yearAwardedBritishDesignerOfTheYear, 1999]
-
A.
BRITAwardYear
Indicates the year in which a particular BRIT Award was given or recognized.
-
B.
yearAwardedTurnerPrize
Indicates the specific year in which an entity received the Turner Prize.
-
C.
OlivierAwardYear
Indicates the specific year in which an Olivier Award was given or associated with a particular recipient, work, or event.
-
D.
tonyAwardsYear
Indicates the year in which a particular Tony Awards event or ceremony took place.
-
E.
bestCostumeDesignWinner
Indicates that the subject is the winner of an award or recognition for best costume design in a given context or event.
- F. None of above. chosen
Provenance (4 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e954a0b8819083e4bd1fa47dc6f5 |
completed | April 7, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69d4dfa657f481909cc5cc8fec00ad19 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e91ce2008190af252c140370b7f2 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 11:58 a.m.