Triple
T6983246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Dover |
E161897
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Calais |
E30799
|
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: Calais | Statement: [Port of Dover, connectsTo, Calais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calais Context triple: [Port of Dover, connectsTo, Calais]
-
A.
Calais
chosen
Calais is a major French port city on the northern coast, serving as one of the primary crossing points between France and England.
-
B.
Calais
Calais is a figure from Greek mythology, one of the winged sons of Boreas who joined Jason and the Argonauts on their legendary voyage.
-
C.
Boulogne
Boulogne is a French football club known for being one of the early professional teams in N’Golo Kanté’s career.
-
D.
Cherbourg
Cherbourg is a major French port city on the Cotentin Peninsula, known for its strategic naval harbor and cross-Channel ferry connections.
-
E.
Honfleur
Honfleur is a historic port town in Normandy, northern France, renowned for its picturesque old harbor, timber-framed houses, and association with Impressionist painters.
- 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_69c68855dc0481909b4c7e9e9ed273db |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db8fdad481908f211a8b333714bd |
completed | March 27, 2026, 7:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c761c671588190a4e7b5c26cdfe6ba |
completed | March 28, 2026, 5:06 a.m. |
Created at: March 27, 2026, 2:31 p.m.