Triple
T32220319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MC |
E823045
|
entity |
| Predicate | hasWellKnownArea |
P3895
|
FINISHED |
| Object | French Riviera |
—
|
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: French Riviera | Statement: [MC, hasWellKnownArea, French Riviera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWellKnownArea Context triple: [MC, hasWellKnownArea, French Riviera]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasIconicArea
chosen
Indicates that an entity possesses a distinct, widely recognized area or region that is emblematic or characteristic of it.
-
C.
hasCoreArea
Indicates that an entity possesses a primary or central area that is fundamental to its structure, function, or focus.
-
D.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
E.
hasMacroArea
Indicates that one entity belongs to, or is located within, a broader geographic or conceptual macro-area represented by 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_69f3490b4f948190b99e4f999f5be25f |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c1bb5f248190834161b5a6ba1ece |
completed | May 3, 2026, 3:32 a.m. |
| PD | Predicate disambiguation | batch_69f6bd25bed08190befcabd3a41ffadf |
completed | May 3, 2026, 3:12 a.m. |
Created at: May 1, 2026, 12:38 a.m.