Triple
T14415643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bottomless Lakes State Park |
E357442
|
entity |
| Predicate | hasHighestUseArea |
P32773
|
FINISHED |
| Object | Lea Lake |
—
|
NE NERFINISHED |
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: Lea Lake | Statement: [Bottomless Lakes State Park, hasHighestUseArea, Lea Lake]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHighestUseArea Context triple: [Bottomless Lakes State Park, hasHighestUseArea, Lea Lake]
-
A.
hasLargestAreaOf
chosen
Indicates that the subject entity possesses the greatest area (size of surface or region) compared to the other entities in the specified set or context.
-
B.
hasCentralAreaUse
Indicates that an entity’s central area is used or designated for a particular function or purpose.
-
C.
hasLargeArea
Indicates that an entity occupies or covers a spatial region whose size exceeds a specified large-area threshold.
-
D.
hasDayUseArea
Indicates that a location or facility includes an area designated for daytime, non-overnight public use.
-
E.
hasMajorCityOfUse
Indicates that a particular city is the primary or most significant location where something (e.g., a product, language, service) is predominantly used or applied.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90cc99208190a2313b1acfb5d802 |
completed | April 14, 2026, 7:09 p.m. |
| PD | Predicate disambiguation | batch_69de5c30467881908e770e3940295641 |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:17 a.m.