Triple
T19482672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tour Part-Dieu |
E487429
|
entity |
| Predicate | skylineStatus |
P56210
|
FINISHED |
| Object | among tallest buildings in Lyon |
—
|
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: among tallest buildings in Lyon | Statement: [Tour Part-Dieu, skylineStatus, among tallest buildings in Lyon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: skylineStatus Context triple: [Tour Part-Dieu, skylineStatus, among tallest buildings in Lyon]
-
A.
skylineType
Indicates the general visual or structural character of a skyline, such as its dominant form, density, or profile.
-
B.
NorikuraSkylineStatus
Indicates the operational status or accessibility condition of the Norikura Skyline road at a given time.
-
C.
peakStatus
Indicates the condition or phase of something at its highest or most intense point in its progression or lifecycle.
-
D.
surfaceStatus
Indicates the condition or state of a surface, such as whether it is intact, damaged, altered, or otherwise characterized.
-
E.
partOfSkylineOf
chosen
Indicates that one entity is a visible component or feature contributing to the overall skyline profile of another entity, typically a city or urban area.
- 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6343ab16481909b508ba0a08ea191 |
completed | April 20, 2026, 2:12 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.