Triple
T10741066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashtown Station |
E253326
|
entity |
| Predicate | hasAmenityRelation |
P66099
|
FINISHED |
| Object | provides access to nearby amenities |
—
|
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: provides access to nearby amenities | Statement: [Ashtown Station, hasAmenityRelation, provides access to nearby amenities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAmenityRelation Context triple: [Ashtown Station, hasAmenityRelation, provides access to nearby amenities]
-
A.
hasAmenityAccessTo
chosen
Indicates that an entity has the right or ability to use or benefit from a specified amenity or facility.
-
B.
hasCivicAmenity
Indicates that an entity possesses, provides, or is associated with a public facility or service intended for community use.
-
C.
hasFacilityType
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
D.
hasUrbanRelation
Indicates a relationship where one entity is connected to another through an urban context, such as city-based location, influence, or interaction.
-
E.
hasResidenceFeature
Indicates that a residence possesses or is characterized by a specific feature or attribute.
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d7104446288190800253f8b652f710 |
completed | April 9, 2026, 2:34 a.m. |
| PD | Predicate disambiguation | batch_69d6f30df9948190ab3cdc33977fac14 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:15 p.m.