Triple
T6846259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edinburgh Princes Street station |
E157901
|
entity |
| Predicate | hadTrainShed |
P13070
|
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: [Edinburgh Princes Street station, hadTrainShed, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadTrainShed Context triple: [Edinburgh Princes Street station, hadTrainShed, yes]
-
A.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
B.
trainsOn
Indicates that one entity receives training, instruction, or practice using or based on another entity (such as a resource, dataset, tool, or subject).
-
C.
trainShedType
chosen
Indicates the specific kind or classification of shed associated with a train or railway facility.
-
D.
hadNo
Indicates that one entity completely lacked or did not possess another entity, attribute, or relationship.
-
E.
usesTrainNumber
Indicates that one entity operates, identifies, or references another entity by a specific train number.
- 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_69c6882ed4c081909dc465a7cf8838be |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d7cbe4488190b41ddf953f12f55f |
completed | March 27, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69c6d09f90648190bc0a462c7d59de1b |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:20 p.m.