Triple
T1909800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashton Moss tram stop |
E38081
|
entity |
| Predicate | hasNearbyRetailPark |
P5648
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Ashton Moss tram stop, hasNearbyRetailPark, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyRetailPark Context triple: [Ashton Moss tram stop, hasNearbyRetailPark, yes]
-
A.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
B.
hasNearbyFacility
chosen
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
C.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
-
D.
hasBusinessPark
Indicates that one entity possesses, contains, or is associated with a business park as part of its facilities or properties.
-
E.
hasRetailArea
Indicates that an entity possesses or includes a designated space used for retail or commercial sales activities.
- 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_69a8862a26088190aae5243695aeefc0 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafeba3d88190afcce67483d8625b |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.