Triple
T22831778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Académie Goncourt |
E565820
|
entity |
| Predicate | mainAwardMonetaryValue |
P25364
|
FINISHED |
| Object | symbolic |
—
|
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: symbolic | Statement: [Académie Goncourt, mainAwardMonetaryValue, symbolic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainAwardMonetaryValue Context triple: [Académie Goncourt, mainAwardMonetaryValue, symbolic]
-
A.
monetaryValue
Indicates the amount of money associated with an entity, event, or transaction.
-
B.
monetaryAwardTypical
Indicates that a monetary award is the usual or standard outcome or component associated with the given context or relationship.
-
C.
monetaryReward
Indicates that one entity provides or promises a payment of money to another as compensation, incentive, or prize.
-
D.
prizeMoneyCurrency
Indicates the currency in which the prize money is denominated or paid.
-
E.
awardAmount
chosen
Indicates the specific quantity or value of an award that is granted in the context of a particular awarding event or relationship.
- 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_69e24585ab1c81909b2b5065d15805d5 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e2bcad8819091f237fd2273a20c |
completed | April 29, 2026, 3:42 a.m. |
| PD | Predicate disambiguation | batch_69eed2d117088190acbfe130d84f8627 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:34 p.m.