Triple
T27746083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capon Springs, West Virginia |
E701985
|
entity |
| Predicate | hasNotableResort |
P10436
|
FINISHED |
| Object | Capon Springs and Farms |
E1785171
|
NE 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: Capon Springs and Farms | Statement: [Capon Springs, West Virginia, hasNotableResort, Capon Springs and Farms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableResort Context triple: [Capon Springs, West Virginia, hasNotableResort, Capon Springs and Farms]
-
A.
hasNotableScenicSpot
Indicates that an entity possesses or is associated with a particularly remarkable or well-known scenic location.
-
B.
hasNotableSkiArea
Indicates that a place or region includes a ski area that is recognized as significant or well-known.
-
C.
hasNotableNationalPark
Indicates that a place or region contains or is associated with a nationally recognized park of particular significance.
-
D.
hasPopularResort
chosen
Indicates that a location or area contains or is associated with a resort that is widely visited or well-liked.
-
E.
hasNotableLodge
Indicates that an entity possesses or is associated with a lodge that is considered notable or significant.
- F. None of above.
Provenance (4 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_69ef6a53c7388190899baa6daf42301c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fec25f0fc48190b87ab1f9cd1eb0de |
completed | May 9, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12ecab3d088190a709ae9a99825391 |
completed | May 24, 2026, 12:18 p.m. |
| PD | Predicate disambiguation | batch_69fec079a770819098df7cc3049df954 |
completed | May 9, 2026, 5:04 a.m. |
Created at: April 27, 2026, 4:16 p.m.