Triple
T4946494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Booker–McConnell |
E111062
|
entity |
| Predicate | sponsoredAwardType |
P1619
|
FINISHED |
| Object | literary prize |
—
|
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: literary prize | Statement: [Booker–McConnell, sponsoredAwardType, literary prize]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsoredAwardType Context triple: [Booker–McConnell, sponsoredAwardType, literary prize]
-
A.
typicalAwardType
Indicates the usual or most common type or category of award associated with a given entity or context.
-
B.
awardType
chosen
Indicates the specific category or kind of award associated with an entity or event.
-
C.
prizeType
Indicates the specific category or kind of prize associated with an entity or event.
-
D.
sponsoringOrganizationType
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
E.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
- 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_69bd441721cc819085c7e33fe0876818 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70abf8dc819090269d0e1ce9f871 |
completed | March 20, 2026, 4:07 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3aa1388190b3e0c8ee1ba1e4fa |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:31 p.m.