Triple
T4946187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Booker Prize |
E111055
|
entity |
| Predicate | longlistSize |
P425
|
FINISHED |
| Object | typically 12 or 13 novels |
—
|
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: typically 12 or 13 novels | Statement: [Booker Prize, longlistSize, typically 12 or 13 novels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: longlistSize Context triple: [Booker Prize, longlistSize, typically 12 or 13 novels]
-
A.
rangeSize
Indicates the extent or magnitude of the range over which something applies, varies, or is distributed.
-
B.
collectionSize
chosen
Indicates the total number of items contained within a specified collection.
-
C.
enrollmentSize
Indicates the number of individuals enrolled or registered in a particular group, program, or institution.
-
D.
sampleSize
Indicates the number of units, observations, or instances included in a particular study, experiment, or dataset.
-
E.
displayCount
Indicates the number of times something is shown or presented, typically within a given context or interface.
- 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_69bd70aa890c81908e685ec5e88cae1f |
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.