Triple
T8522517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bicester Village railway station |
E201725
|
entity |
| Predicate | hasUsePattern |
P83136
|
FINISHED |
| Object | high usage by shoppers |
—
|
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: high usage by shoppers | Statement: [Bicester Village railway station, hasUsePattern, high usage by shoppers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUsePattern Context triple: [Bicester Village railway station, hasUsePattern, high usage by shoppers]
-
A.
hasPattern
Indicates that one entity exhibits, follows, or is characterized by a specific recurring form, structure, or design defined by another entity.
-
B.
hasUseCase
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with another entity.
-
C.
hasPlanningPattern
Indicates that an entity follows or is associated with a particular planning pattern or structured approach to planning.
-
D.
hasFormerUse
Indicates that something previously served a particular function or role that it no longer has.
-
E.
hasBuildingPattern
Indicates that an entity exhibits or follows a particular architectural or structural building pattern.
- F. None of above. chosen
Provenance (4 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe64215408190b45f462a32d3471d |
completed | March 31, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cbd10f64b4819080859057c19e58f0 |
completed | March 31, 2026, 1:50 p.m. |
| PDg | Predicate description generation | batch_69cbe30d453481908f897ed2b06e7534 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:16 p.m.