Triple
T15966980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derby du Nord |
E387215
|
entity |
| Predicate | derbyNameLanguage |
P13426
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Derby du Nord, derbyNameLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: derbyNameLanguage Context triple: [Derby du Nord, derbyNameLanguage, French]
-
A.
derby
Indicates a competitive match or contest, typically between closely linked or rival entities (such as teams from the same area or group).
-
B.
hasDemonymLanguage
Indicates that a language is used as the demonym (people’s name or adjective of nationality) for inhabitants of a particular place or group.
-
C.
derbyFocus
Indicates that something is the main subject, theme, or point of attention within the context of a derby event or competition.
-
D.
languageName
chosen
Indicates the specific name assigned to a language in the relationship.
-
E.
typeOfDerby
Indicates that one entity is a specific kind or category of derby in relation to another entity.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.