Triple
T27601522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Lake County |
E700055
|
entity |
| Predicate | hasNotableRecreation |
P971
|
FINISHED |
| Object | boating on Green Lake |
—
|
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: boating on Green Lake | Statement: [Green Lake County, hasNotableRecreation, boating on Green Lake]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableRecreation Context triple: [Green Lake County, hasNotableRecreation, boating on Green Lake]
-
A.
hasRecreationActivity
chosen
Indicates that an entity provides, includes, or is associated with a particular recreational activity.
-
B.
hasRecreationalAspect
Indicates that something includes, involves, or is characterized by a recreational or leisure-related component or purpose.
-
C.
hasRecreationPurpose
Indicates that something is used or intended to be used for recreational or leisure activities.
-
D.
hasRecreationalContext
Indicates that something occurs, is used, or is understood within a leisure, entertainment, or recreational setting or purpose.
-
E.
hasRecreationalArea
Indicates that an entity includes, provides, or is associated with a designated space intended for leisure or recreational activities.
- 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_69ef6a4e2e208190b63b7268f405785c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69feafa1ba0081909013800b85a9f613 |
completed | May 9, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69feae58d62c81909d031f3df8992883 |
completed | May 9, 2026, 3:47 a.m. |
Created at: April 27, 2026, 2:08 p.m.