Triple
T14980935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grenelle |
E373571
|
entity |
| Predicate | urbanProfile |
P93343
|
FINISHED |
| Object | mixed residential and commercial area |
—
|
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: mixed residential and commercial area | Statement: [Grenelle, urbanProfile, mixed residential and commercial area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanProfile Context triple: [Grenelle, urbanProfile, mixed residential and commercial area]
-
A.
urbanStatusContext
Indicates the relationship between an entity and the urban characteristics or conditions of its surrounding environment or context.
-
B.
provinceMetropolitanSee
Indicates that a metropolitan see (an archdiocesan seat) has ecclesiastical jurisdiction over, or is the principal see of, a given church province.
-
C.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
D.
cityFunction
chosen
Indicates the primary role, purpose, or functional classification associated with a city (e.g., administrative, commercial, industrial, cultural).
-
E.
urbanRole
Indicates the function, status, or role that an entity holds within an urban or city context.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6fcebf481909f72cab577560d82 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:52 a.m.