Triple
T9939051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Menton |
E194030
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Bad Säckingen |
E645232
|
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: Bad Säckingen | Statement: [Menton, twinnedWith, Bad Säckingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Säckingen Context triple: [Menton, twinnedWith, Bad Säckingen]
-
A.
Bad Säckingen
chosen
Bad Säckingen is a historic spa town in southwestern Germany on the Rhine River, known for its medieval old town and one of the longest covered wooden bridges in Europe.
-
B.
Bad Cannstatt
Bad Cannstatt is a historic district of Stuttgart, Germany, known for its mineral springs, traditional architecture, and the Cannstatter Volksfest beer festival.
-
C.
Bad Schussenried
Bad Schussenried is a spa town in southern Germany known for its historic monastery complex and scenic location in Upper Swabia.
-
D.
Bad Heilbrunn
Bad Heilbrunn is a spa municipality in Upper Bavaria, Germany, known for its health resorts and scenic Alpine foothills setting.
-
E.
Schaafheim
Schaafheim is a municipality in the state of Hesse in central Germany.
- 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_69ca82e409348190a393777356b80a2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb5e819e08190967b799fd236749e |
completed | April 2, 2026, 12:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e531dfa48190b57fcd2444de1ab7 |
completed | April 5, 2026, 10:41 p.m. |
Created at: March 30, 2026, 8:44 p.m.