Triple
T14162531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Town/Chinatown MAX Station |
E350983
|
entity |
| Predicate | hasStreetLocation |
P17242
|
FINISHED |
| Object | NW 1st Avenue |
—
|
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: NW 1st Avenue | Statement: [Old Town/Chinatown MAX Station, hasStreetLocation, NW 1st Avenue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStreetLocation Context triple: [Old Town/Chinatown MAX Station, hasStreetLocation, NW 1st Avenue]
-
A.
hasStreet
Indicates that an entity is located on, associated with, or identified by a particular street.
-
B.
streetLocation
chosen
Indicates that one entity is located on, along, or at a specific street associated with the other entity.
-
C.
hasStreetLevel
Indicates that something is located at, accessible from, or directly associated with the street level of a building or area.
-
D.
hasNotableStreet
Indicates that an entity is associated with a particular street that is considered notable or significant.
-
E.
hasNearbyStreet
Indicates that one entity is located close to or adjacent to a street.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de613a4a2081908fd51bf4b4d82b6c |
completed | April 14, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69de05b8434c81908c33b1b513463b12 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 12:59 a.m.