Triple
T2511534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bellingham metropolitan area |
E52709
|
entity |
| Predicate | distanceToCity |
P7750
|
FINISHED |
| Object | approximately 90 miles north of Seattle |
—
|
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: approximately 90 miles north of Seattle | Statement: [Bellingham metropolitan area, distanceToCity, approximately 90 miles north of Seattle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCity Context triple: [Bellingham metropolitan area, distanceToCity, approximately 90 miles north of Seattle]
-
A.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
B.
distanceCategory
Indicates the qualitative classification of how far apart two entities are from each other (e.g., near, medium, far).
-
C.
distanceCharacteristic
Indicates a relationship where an entity is described or constrained by some property or measure of distance (e.g., range, spacing, or separation).
-
D.
distance
chosen
Indicates the spatial separation or length between two points, objects, or locations.
-
E.
distanceToNewYorkCity
Indicates the spatial distance between a given entity’s location and New York City.
- 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_69ab4958e76481908a235377dd921c9e |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd5a33234819082ad49fa6594b6be |
completed | March 7, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69abd0bf37c0819088d28b5081ba7556 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.