Triple
T14967220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3rd Avenue (Seattle) |
E373219
|
entity |
| Predicate | hasNearbyLandmarkType |
P90437
|
FINISHED |
| Object | office towers |
—
|
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: office towers | Statement: [3rd Avenue (Seattle), hasNearbyLandmarkType, office towers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyLandmarkType Context triple: [3rd Avenue (Seattle), hasNearbyLandmarkType, office towers]
-
A.
proximityToLandmark
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
B.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
C.
typicalNearbyLandmarks
chosen
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
D.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
E.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e44cb0819096e09f8026ef8174 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a5d995881909e33658f5aea5582 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:47 a.m.