Triple
T10296262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biella |
E241495
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Saarbrücken |
E269297
|
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: Saarbrücken | Statement: [Biella, twinTown, Saarbrücken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saarbrücken Context triple: [Biella, twinTown, Saarbrücken]
-
A.
Saarbrücken
chosen
Saarbrücken is a German city on the Saar River known as an industrial, cultural, and educational center near the French border.
-
B.
Saarlouis
Saarlouis is a town in the German state of Saarland, known historically as a fortified city founded by Louis XIV of France near the French border.
-
C.
Wissembourg
Wissembourg is a historic town in northeastern France’s Alsace region, known for its well-preserved medieval architecture and proximity to the German border.
-
D.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
-
E.
Lörrach
Lörrach is a town in southwest Germany’s Baden-Württemberg state, near the borders with Switzerland and France, known for its proximity to Basel and its role as a regional economic and cultural center.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ea9b3c8190b11518b259d5825c |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3734e5e688190bbfa472547ef65e8 |
completed | April 18, 2026, 12:04 p.m. |
Created at: April 6, 2026, 11:43 a.m.