Triple
T15283734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanseatic cities |
E365339
|
entity |
| Predicate | hasImportantMember |
P304
|
FINISHED |
| Object | Bruges |
E41564
|
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: Bruges | Statement: [Hanseatic cities, hasImportantMember, Bruges]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bruges Context triple: [Hanseatic cities, hasImportantMember, Bruges]
-
A.
Bruges
chosen
Bruges is a historic Belgian city renowned for its well-preserved medieval architecture, picturesque canals, and rich artistic heritage.
-
B.
Bruges
Bruges is a commune in southwestern France, located near the city of Bordeaux in the Gironde department.
-
C.
Ghent
Ghent is a historic city in the Flemish region of Belgium, known for its medieval architecture, canals, and role as a major cultural and economic center in the Middle Ages.
-
D.
Ghent
Ghent is a small unincorporated community and ski-area destination located in Raleigh County, West Virginia, United States.
-
E.
Brussels
Brussels is a small unincorporated community and town in Door County, Wisconsin, known for its strong Belgian-American heritage.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e53c9588190a6cb61ac8805c706 |
completed | April 15, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef798a588190981c77e6f4c6be78 |
completed | May 9, 2026, 8:25 a.m. |
Created at: April 10, 2026, 3:15 a.m.