Triple
T18250387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosen College of Hospitality Management |
E437072
|
entity |
| Predicate | hasCloseTiesToIndustry |
P110684
|
FINISHED |
| Object | Central Florida tourism sector |
—
|
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: Central Florida tourism sector | Statement: [Rosen College of Hospitality Management, hasCloseTiesToIndustry, Central Florida tourism sector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCloseTiesToIndustry Context triple: [Rosen College of Hospitality Management, hasCloseTiesToIndustry, Central Florida tourism sector]
-
A.
hasIndustryTies
chosen
Indicates that an entity maintains professional, financial, or organizational connections with a particular industry or sector.
-
B.
hasRelativeInSameIndustry
Indicates that one entity has a relative who works in the same industry as the entity.
-
C.
hasNearbyIndustry
Indicates that an entity is located close to one or more industrial facilities or activities.
-
D.
relationToIndustry
Indicates how an entity is connected or relevant to a particular industry, such as through involvement, impact, or association.
-
E.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd8065b08190ae8d37102141f470 |
completed | April 19, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69e44fcdee748190bae6fb76e0cb22f3 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:33 a.m.