Triple
T7375725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | canton of Calais-2 |
E170116
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Calais-2@fr |
E170116
|
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-2@fr | Statement: [canton of Calais-2, hasNameInLanguage, Calais-2@fr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calais-2@fr Context triple: [canton of Calais-2, hasNameInLanguage, Calais-2@fr]
-
A.
Calaisienne
Calaisienne is the French term for a female inhabitant or native of the port city of Calais in northern France.
-
B.
Calais
Calais is a major French port city on the northern coast, serving as one of the primary crossing points between France and England.
-
C.
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.
-
D.
canton of Calais-2
chosen
The canton of Calais-2 is an administrative division in the Pas-de-Calais department in northern France, encompassing part of the city of Calais and surrounding areas.
-
E.
Villeneuve d’Ascq
Villeneuve d’Ascq is a suburban city in northern France near Lille, known for its universities, technology parks, and modernist urban planning.
- 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_69c68a5bfaac81909ce7f001dfb70c76 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1a780f88190abf11994e307b6ad |
completed | March 27, 2026, 9:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c810e3c2bc8190a455dac60dfdbc97 |
completed | March 28, 2026, 5:33 p.m. |
Created at: March 27, 2026, 3:07 p.m.