Triple
T27538652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Steel Building (informal, historical) |
E695167
|
entity |
| Predicate | refersToHeightFeet |
P30891
|
FINISHED |
| Object | 743 |
—
|
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: 743 | Statement: [U.S. Steel Building (informal, historical), refersToHeightFeet, 743]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToHeightFeet Context triple: [U.S. Steel Building (informal, historical), refersToHeightFeet, 743]
-
A.
heightFeet
chosen
Indicates a relationship where a subject has its vertical size or stature specified in feet as a unit of measurement.
-
B.
hasLengthFeet
Indicates that an entity possesses a length measured in feet.
-
C.
heightApproximateFeet
Indicates that one entity’s height is approximately equal to a specified value measured in feet.
-
D.
heightReference
Indicates that one entity’s height is being measured, compared, or defined relative to another specified reference point or standard.
-
E.
depthFeetApprox
Indicates an approximate measurement of an entity’s depth expressed in feet.
- 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_69ef538608b081908b9f659bb09d5e0f |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 27, 2026, 1:29 p.m.