Triple
T30086244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Lake Union, Seattle, Washington, United States |
E764608
|
entity |
| Predicate | hasNeighborhoodPark |
P162196
|
FINISHED |
| Object | Lake Union Park |
—
|
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: Lake Union Park | Statement: [South Lake Union, Seattle, Washington, United States, hasNeighborhoodPark, Lake Union Park]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeighborhoodPark Context triple: [South Lake Union, Seattle, Washington, United States, hasNeighborhoodPark, Lake Union Park]
-
A.
isNeighborhoodParkOf
chosen
Indicates that a park is located within and serves as a local recreational area for a specific neighborhood.
-
B.
hasNearbyGreenSpace
Indicates that an entity is located close to an area of green space, such as a park, garden, or natural vegetation.
-
C.
hasNearbyProvincialPark
Indicates that one entity is located close to, or in the vicinity of, a provincial park.
-
D.
nearRetailPark
Indicates that one entity is located close to or in the immediate vicinity of a retail park.
-
E.
hasNearbyStatePark
Indicates that a location is situated close to at least one designated state park.
- 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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fe8ddf70e48190a917eb9e8f7b6966 |
completed | May 9, 2026, 1:29 a.m. |
| PD | Predicate disambiguation | batch_69fe87ef94dc81909bb00ec8d6de9bcd |
completed | May 9, 2026, 1:03 a.m. |
Created at: April 29, 2026, 7:04 p.m.