Triple
T6836880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cardiff Queen Street railway station |
E157471
|
entity |
| Predicate | hasEntranceOn |
P1974
|
FINISHED |
| Object | Queen Street |
E227017
|
NE 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: Queen Street | Statement: [Cardiff Queen Street railway station, hasEntranceOn, Queen Street]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Queen Street Context triple: [Cardiff Queen Street railway station, hasEntranceOn, Queen Street]
-
A.
Queen Street
Queen Street is the main commercial and retail thoroughfare in central Auckland, New Zealand, known for its shops, offices, and entertainment venues.
-
B.
Queen Street
chosen
Queen Street is a major commercial thoroughfare in central Cardiff, Wales, known for its busy shopping area and pedestrianized zone.
-
C.
Queen Street
Queen Street is a central shopping and pedestrian thoroughfare in Oxford, England, linking the city’s main commercial and historic areas.
-
D.
Queen Street
Queen Street is a major east–west thoroughfare in Toronto, Ontario, known for its diverse neighborhoods, shopping, and cultural attractions.
-
E.
Queen Street
Queen Street is a notable thoroughfare in Salisbury, England, known for its central location and mix of historic and commercial buildings.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6882c53608190b99aebef079b23bd |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d67c1c508190ab39b8aaaaacc628 |
completed | March 27, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723ffce448190ac8edbaaa1517972 |
completed | March 28, 2026, 12:42 a.m. |
Created at: March 27, 2026, 2:19 p.m.