Triple
T26536175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Bend, Washington |
E671254
|
entity |
| Predicate | Twede’s CafeAlsoKnownAs |
P118970
|
FINISHED |
| Object | Double R Diner filming location |
—
|
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: Double R Diner filming location | Statement: [North Bend, Washington, Twede’s CafeAlsoKnownAs, Double R Diner filming location]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: Twede’s CafeAlsoKnownAs Context triple: [North Bend, Washington, Twede’s CafeAlsoKnownAs, Double R Diner filming location]
-
A.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
B.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
C.
venueAlsoKnownAs
chosen
Indicates that a venue has an alternative name or alias by which it is also known.
-
D.
rivalRestaurantOf
Indicates that one restaurant competes with another restaurant in the same market or customer base.
-
E.
alsoEats
Indicates that an entity consumes something in addition to another item or items it already eats.
- 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_69eeb3206e748190b90c85cc81f38c91 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f613fc19d08190a90a8dcac0b8e8d5 |
completed | May 2, 2026, 3:10 p.m. |
| PD | Predicate disambiguation | batch_69f602d7b1b0819095ddd3b5169f8ce2 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 27, 2026, 1:38 a.m.