Triple
T34508837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Promenade Sussex |
E885964
|
entity |
| Predicate | frenchNameOf |
P6538
|
FINISHED |
| Object | Sussex Drive |
—
|
NE NERFINISHED |
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: Sussex Drive | Statement: [Promenade Sussex, frenchNameOf, Sussex Drive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frenchNameOf Context triple: [Promenade Sussex, frenchNameOf, Sussex Drive]
-
A.
nameInFrench
chosen
Indicates that an entity is known or referred to by a specific name expressed in the French language.
-
B.
FrenchForm
Indicates that one entity is a form, version, or expression of another specifically in the French language.
-
C.
isFrancophoneCounterpartOf
Indicates that one entity serves as the French-speaking or French-language equivalent or counterpart of another entity.
-
D.
FrenchComponent
Indicates that something is a component, part, or element that is specifically French in origin, language, or context.
-
E.
correspondsToAbbreviationInFrench
Indicates that one entity is the full form or expression for which the other entity serves as the abbreviation in French.
- 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_69f349cc0220819081f154c6964f4dc2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f727bde8f88190ad746ca515134ca1 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72739c30c81908642eef3feb3afcf |
completed | May 3, 2026, 10:45 a.m. |
Created at: May 1, 2026, 2:01 a.m.