Triple
T2943488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pensacola Beach Air Show |
E79441
|
entity |
| Predicate | transportImpact |
P3830
|
FINISHED |
| Object | increased traffic on Pensacola Beach |
—
|
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: increased traffic on Pensacola Beach | Statement: [Pensacola Beach Air Show, transportImpact, increased traffic on Pensacola Beach]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transportImpact Context triple: [Pensacola Beach Air Show, transportImpact, increased traffic on Pensacola Beach]
-
A.
transportationImpact
chosen
Indicates how one entity’s transportation-related activities or characteristics affect another entity or the surrounding environment.
-
B.
hasEnvironmentalImpactOn
Indicates that one entity affects or alters the environmental conditions, quality, or ecological state of another entity.
-
C.
transportation
Indicates the movement of someone or something from one place to another, typically using a vehicle or transit system.
-
D.
socialImpact
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
E.
hasTourismImpactOn
Indicates that one entity affects or influences the tourism levels, patterns, or attractiveness of another entity.
- 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_69ad8b1089588190b74d9e2505e45762 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9871fc908190ad90e5b01b476b3f |
completed | March 8, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69ad96088fb481909976b436c2b729d9 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:56 p.m.