Triple
T15362164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago Federal Center |
E367313
|
entity |
| Predicate | floorCountOfPart |
P1514
|
FINISHED |
| Object | Dirksen Federal Building: 30 stories |
—
|
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: Dirksen Federal Building: 30 stories | Statement: [Chicago Federal Center, floorCountOfPart, Dirksen Federal Building: 30 stories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: floorCountOfPart Context triple: [Chicago Federal Center, floorCountOfPart, Dirksen Federal Building: 30 stories]
-
A.
floorCount
chosen
Indicates the number of floors or levels that a building or structure has.
-
B.
floorCountApproximate
Indicates an approximate or estimated number of floors associated with a building or structure.
-
C.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
-
D.
floorHeight
Indicates the vertical elevation or level at which a particular floor is positioned within a structure.
-
E.
locatedInBuildingFloorCount
Indicates that one entity is located in or associated with a building characterized by a specific number of floors.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e479f188190bbbc3dcd73853e02 |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:18 a.m.