Triple
T29307841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union Station (Columbus, Ohio) |
E743145
|
entity |
| Predicate | hadDesignInfluence |
P44042
|
FINISHED |
| Object | City Beautiful movement aesthetics |
—
|
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: City Beautiful movement aesthetics | Statement: [Union Station (Columbus, Ohio), hadDesignInfluence, City Beautiful movement aesthetics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadDesignInfluence Context triple: [Union Station (Columbus, Ohio), hadDesignInfluence, City Beautiful movement aesthetics]
-
A.
designInfluenceOn
chosen
Indicates that one design, designer, or design-related factor has an effect on shaping, guiding, or altering another design or design outcome.
-
B.
hadInfluenceOn
Indicates that one entity affected, shaped, or contributed to the development, behavior, or characteristics of another entity.
-
C.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
D.
hasEnduringInfluenceOn
Indicates that one entity exerts a lasting, long-term impact on another entity’s state, development, or behavior.
-
E.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
- 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_69f09123ed9881909f351f7541933f5e |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f665a7fd548190b0cf946b6bc8710b |
completed | May 2, 2026, 8:59 p.m. |
| PD | Predicate disambiguation | batch_69f660f2e3708190ab658652bcfc04d0 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 28, 2026, 1:14 p.m.