Triple
T27538664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Steel Building (informal, historical) |
E695167
|
entity |
| Predicate | refersToNearbySite |
P180212
|
FINISHED |
| Object | World Trade Center site |
—
|
NE NERFINISHED |
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: World Trade Center site | Statement: [U.S. Steel Building (informal, historical), refersToNearbySite, World Trade Center site]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToNearbySite Context triple: [U.S. Steel Building (informal, historical), refersToNearbySite, World Trade Center site]
-
A.
nearbySitePartOf
Indicates that one site or site component is located close to and is considered part of another, larger site or site component.
-
B.
hasNearbySiteType
Indicates that one entity has another entity of a specified site type located in its close physical vicinity.
-
C.
notableNearbySite
Indicates that one entity is a significant or noteworthy site located close to another entity.
-
D.
linkedSite
Indicates that one site has an explicit hyperlink or reference connection to another site.
-
E.
nearSiteOf
Indicates that one entity is located in close physical proximity to the site or location associated with another entity.
- 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_69ef538608b081908b9f659bb09d5e0f |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
| PDg | Predicate description generation | batch_69f739a58b3c81908abc2b8738a65678 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 27, 2026, 1:29 p.m.