Triple
T67851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eiffel Tower |
E1351
|
entity |
| Predicate | hasGlassFloor |
P3727
|
FINISHED |
| Object | yes (first level) |
—
|
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: yes (first level) | Statement: [Eiffel Tower, hasGlassFloor, yes (first level)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGlassFloor Context triple: [Eiffel Tower, hasGlassFloor, yes (first level)]
-
A.
floorCount
Indicates the number of floors or levels that a building or structure has.
-
B.
observationDeckFloor
Indicates the specific floor or level of a building on which an observation deck is located.
-
C.
hasEntranceOn
Indicates that one entity’s entrance or access point is located on or faces a specified side, boundary, or feature of another entity.
-
D.
hasElevators
Indicates that one entity is equipped with or contains one or more elevators for vertical transportation.
-
E.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24ea8cfd081908a26edad2473dde3 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a24fcf5a88819088c5fa4c08476358 |
completed | Feb. 28, 2026, 2:15 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.