Triple
T4946488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Booker–McConnell |
E111062
|
entity |
| Predicate | fieldOfSponsorship |
P49315
|
FINISHED |
| Object | literature |
—
|
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: literature | Statement: [Booker–McConnell, fieldOfSponsorship, literature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldOfSponsorship Context triple: [Booker–McConnell, fieldOfSponsorship, literature]
-
A.
sponsorField
chosen
Indicates that one entity acts as a sponsor specifically in the context of a particular field, domain, or area associated with another entity.
-
B.
sponsoringInstitution
Indicates that an institution provides financial or organizational support to enable or underwrite an activity, project, event, or entity.
-
C.
sponsoringOrganizationType
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
D.
fieldOfSignificance
Indicates that something holds particular importance, relevance, or impact within a specified domain, context, or area of interest.
-
E.
sponsorshipScope
Indicates the extent, boundaries, or specific aspects of an activity, event, or entity that a sponsor’s support or involvement covers.
- 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.