Triple
T10298936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rue Cler |
E241571
|
entity |
| Predicate | cityQuarterCharacter |
P92978
|
FINISHED |
| Object | residential and commercial mix |
—
|
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: residential and commercial mix | Statement: [Rue Cler, cityQuarterCharacter, residential and commercial mix]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityQuarterCharacter Context triple: [Rue Cler, cityQuarterCharacter, residential and commercial mix]
-
A.
neighborCharacter
Indicates that one character is located adjacent to or next to another character in a given context.
-
B.
situatedInQuarter
Indicates that one entity is located within or belongs to a specific quarter or district of a larger area.
-
C.
cityPatron
Indicates that one entity serves as the patron, protector, or special guardian of a particular city.
-
D.
touristCharacter
Indicates that an entity has the role, behavior, or qualities characteristic of a tourist in relation to another entity or context.
-
E.
urbanComponent
Indicates that something functions as a constituent part or element within an urban area or city system.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ee10f88190b1615c49b8f24a26 |
completed | April 7, 2026, 9:48 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f35e548190be3b4d92d65d2d20 |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d29d7cf08190acd70cee634c5cdb |
completed | April 7, 2026, 9:47 a.m. |
Created at: April 6, 2026, 11:44 a.m.