Triple
T8983930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blount County |
E214606
|
entity |
| Predicate | hasHistoricAttractionType |
P8648
|
FINISHED |
| Object | covered bridge tours |
—
|
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: covered bridge tours | Statement: [Blount County, hasHistoricAttractionType, covered bridge tours]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricAttractionType Context triple: [Blount County, hasHistoricAttractionType, covered bridge tours]
-
A.
hasHistoricSite
Indicates that an entity possesses, contains, or is associated with a place recognized for its historical significance.
-
B.
hasAttractionType
chosen
Indicates that one entity is associated with a specific kind or category of attraction (e.g., tourist, cultural, natural).
-
C.
hasHistoricalCategory
Indicates that something is associated with a particular historical classification, period, or type based on its past context or significance.
-
D.
historicLocationType
Indicates the specific kind or category of a place based on its historical significance or role.
-
E.
hasHistoricResortOrigin
Indicates that something originated as, or was originally established for use as, a historic resort.
- 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_69ca839f76bc8190a4b7123cdd682199 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc67eb3cfc8190900a8253cc44621c |
completed | April 1, 2026, 12:33 a.m. |
| PD | Predicate disambiguation | batch_69cc5edba0f88190b97401636a076d7a |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:03 p.m.