Triple
T107562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Champs-Élysées |
E2172
|
entity |
| Predicate | urbanDevelopment |
P7886
|
FINISHED |
| Object | extended and redesigned in the 19th century |
—
|
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: extended and redesigned in the 19th century | Statement: [Champs-Élysées, urbanDevelopment, extended and redesigned in the 19th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanDevelopment Context triple: [Champs-Élysées, urbanDevelopment, extended and redesigned in the 19th century]
-
A.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
B.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
C.
neighborhood
Indicates that one entity is located in close spatial proximity to another, typically within the same local area or district.
-
D.
city1
Indicates that the subject is classified as a city.
-
E.
notableCityCommunity
Indicates a relationship where a city is notably associated with, recognized by, or significantly connected to a particular community.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25a1199ac8190ac65ffaaf45b4f5b |
completed | Feb. 28, 2026, 2:59 a.m. |
| PD | Predicate disambiguation | batch_69a2563e7188819091e9a94e071991d7 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a25a10d6448190bee47847d5c13b84 |
completed | Feb. 28, 2026, 2:59 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.