Triple
T17801943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rue de Passy |
E444448
|
entity |
| Predicate | hasCommercialMix |
P79715
|
FINISHED |
| Object | international brands |
—
|
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: international brands | Statement: [Rue de Passy, hasCommercialMix, international brands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialMix Context triple: [Rue de Passy, hasCommercialMix, international brands]
-
A.
commercialMix
chosen
Indicates a relationship where different commercial elements, such as products, services, or marketing components, are combined or integrated into a single offering or context.
-
B.
hasCommercialVariant
Indicates that an entity has a related version that is produced, marketed, or sold commercially.
-
C.
hasRemix
Indicates that one creative work is a remix version derived from or based on another work.
-
D.
hasCommercialField
Indicates that one entity possesses or is associated with a commercial-related field, area, or domain in relation to another entity.
-
E.
hasCommercialStrip
Indicates that an area or entity contains or is associated with a zone characterized by a concentration of commercial businesses or retail establishments.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e488005a288190b7a2cffa590d2557 |
completed | April 19, 2026, 7:45 a.m. |
| PD | Predicate disambiguation | batch_69e3d8de28688190844b65acf6af54e6 |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:13 a.m.