Triple
T21348969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avenue de la Motte-Picquet |
E526421
|
entity |
| Predicate | districtCharacter |
P30854
|
FINISHED |
| Object | upscale neighborhood |
—
|
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: upscale neighborhood | Statement: [Avenue de la Motte-Picquet, districtCharacter, upscale neighborhood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: districtCharacter Context triple: [Avenue de la Motte-Picquet, districtCharacter, upscale neighborhood]
-
A.
cityQuarterCharacter
Indicates the characteristic qualities or distinctive nature that define a particular city quarter.
-
B.
neighborCharacter
Indicates that one character is located adjacent to or next to another character in a given context.
-
C.
regionCharacter
chosen
Indicates a characteristic, feature, or quality that typifies or defines a particular region.
-
D.
hasNotableResident
Indicates that an entity is or has been a well-known or distinguished resident of a particular place or location.
-
E.
residentInFiction
Indicates that one entity is a fictional character or element that resides or exists within the fictional setting, world, or universe 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_69e0b51cd5cc81909ac1187971e8a8ad |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee5baab4e081908916a289c607cf3a |
completed | April 26, 2026, 6:38 p.m. |
| PD | Predicate disambiguation | batch_69e6161feea4819091d13bb003363279 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:02 p.m.