Triple
T23061740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Welcome (2009 film) |
E574919
|
entity |
| Predicate | settingLocation |
P40
|
FINISHED |
| Object | Calais |
—
|
NE NERFINISHED |
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: [Welcome (2009 film), settingLocation, Calais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calais Context triple: [Welcome (2009 film), settingLocation, 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.
Cherbourg
Cherbourg is a rural Aboriginal community in southern Queensland, Australia, known for its significant Indigenous history and culture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1899ff96081908d89a07a3b1065c8 |
completed | April 29, 2026, 4:31 a.m. |
Created at: April 17, 2026, 3:55 p.m.