Triple
T4946497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Booker–McConnell |
E111062
|
entity |
| Predicate | sponsoredAwardLanguage |
P3681
|
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: [Booker–McConnell, sponsoredAwardLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsoredAwardLanguage Context triple: [Booker–McConnell, sponsoredAwardLanguage, English]
-
A.
awardNameLanguage
Indicates the language in which the name of an award is expressed.
-
B.
winnerLanguage
Indicates that the associated language is the one used by, or officially recognized for, the winner in a given contest, award, or competitive event.
-
C.
awardLocale
Indicates the place or geographic location where an award is given or recognized.
-
D.
presentedInLanguage
chosen
Indicates that something (such as content, information, or a work) is expressed or made available using a particular language.
-
E.
awardFor
Indicates that something is given or granted as recognition or a prize for a particular achievement, work, or contribution.
- 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.