Triple
T21689549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston Custom House (original 1840s building) |
E535321
|
entity |
| Predicate | visualDominance |
P70902
|
FINISHED |
| Object | prominent feature of Boston skyline |
—
|
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: prominent feature of Boston skyline | Statement: [Boston Custom House (original 1840s building), visualDominance, prominent feature of Boston skyline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualDominance Context triple: [Boston Custom House (original 1840s building), visualDominance, prominent feature of Boston skyline]
-
A.
visualImportance
Indicates how visually prominent or attention‑drawing one entity is relative to others in a given context.
-
B.
visualSimplicity
Indicates that something is characterized by a minimal, uncluttered, and easy-to-perceive visual appearance or design.
-
C.
visualDetail
Indicates that one entity provides or specifies the visual characteristics, features, or appearance details of another entity.
-
D.
visualCenterpiece
chosen
Indicates that one entity serves as the primary visual focus or dominant visual element in relation to another entity.
-
E.
vision
Indicates that an entity perceives another entity or object visually, using sight.
- 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_69e0c469b6ec8190aee4cadd1527db91 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef96ce2ff88190a6cbfff45bb6a04f |
completed | April 27, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69e6969113cc8190ab69855ef5667e4b |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:44 p.m.