Triple
T67820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eiffel Tower |
E1351
|
entity |
| Predicate | elevationOfFirstLevel |
P221
|
FINISHED |
| Object | 57 metres |
—
|
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: 57 metres | Statement: [Eiffel Tower, elevationOfFirstLevel, 57 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: elevationOfFirstLevel Context triple: [Eiffel Tower, elevationOfFirstLevel, 57 metres]
-
A.
elevation
chosen
Indicates the vertical height or altitude of one entity relative to a reference level or another entity.
-
B.
typicalElevationRange
Indicates the usual range of elevation values within which something commonly occurs or exists.
-
C.
numberOfBasementLevels
Indicates the total count of basement levels associated with a given structure or property.
-
D.
highestPoint
Indicates that one entity is the point with the greatest elevation or height relative to another entity or defined area.
-
E.
lowestPoint
Indicates that one entity is the point with the minimum vertical position or value relative to another entity or within a specified 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a2509b5a088190bb9d2b650aeb8bca |
completed | Feb. 28, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69a24ea749788190bc17865171ff909a |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.