Triple
T3886947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tour Bretagne |
E92963
|
entity |
| Predicate | rankingInCityByHeight |
P31595
|
FINISHED |
| Object | one of the tallest buildings in Nantes |
—
|
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: one of the tallest buildings in Nantes | Statement: [Tour Bretagne, rankingInCityByHeight, one of the tallest buildings in Nantes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingInCityByHeight Context triple: [Tour Bretagne, rankingInCityByHeight, one of the tallest buildings in Nantes]
-
A.
rankInCityByHeight
Indicates the relative ordering of entities within a specific city based on their height, such as which is tallest, second tallest, and so on.
-
B.
rankAmongTallestBuildings
chosen
Indicates that one building is among the tallest buildings within a specified group, area, or category.
-
C.
rankInShanghaiByHeightCurrent
Indicates the position an entity currently holds in a ranking of heights within Shanghai, ordered from tallest to shortest.
-
D.
buildingHeight
Indicates the vertical extent or height measurement of a building.
-
E.
buildingHeightContext
Indicates the contextual or situational factors under which a building’s height is defined, measured, or interpreted.
- 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_69aed9697de0819087c2559295ff3d12 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeecabe3548190a5cbf9d0af0bcfb6 |
completed | March 9, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69aee759609c8190985e96ec6d96dedd |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:20 p.m.