Triple
T18110618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1000 Guineas Stakes |
E433462
|
entity |
| Predicate | isClassicNumber |
P130485
|
FINISHED |
| Object | second of the five British Classics |
—
|
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: second of the five British Classics | Statement: [1000 Guineas Stakes, isClassicNumber, second of the five British Classics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isClassicNumber Context triple: [1000 Guineas Stakes, isClassicNumber, second of the five British Classics]
-
A.
hasCanonicalNumber
Indicates that an entity is associated with its officially recognized or standard reference number.
-
B.
hasNumberCategory
Indicates that an entity is associated with a specific numerical classification or type.
-
C.
isStandard
Indicates that something conforms to an established norm, specification, or commonly accepted rule.
-
D.
isClassicTrialFor
Indicates that one entity is a canonical or prototypical example of a trial or test used for evaluating, demonstrating, or studying the other entity.
-
E.
hasNumberDistinction
Indicates that a language or system grammatically distinguishes between different numbers (such as singular, plural, dual, etc.) in its expressions.
- F. None of above. chosen
Provenance (4 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_69d8b90916008190a1f110bd7ced5473 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd2038081909515fb6d17495cbf |
completed | April 19, 2026, 1:51 p.m. |
| PD | Predicate disambiguation | batch_69e43313ca788190baa224269e71de49 |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f5ae2c8190b11dee46534fa5a9 |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:28 a.m.