Triple
T18163523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobahn A61 |
E434827
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Bad Kreuznach |
—
|
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: Bad Kreuznach | Statement: [Autobahn A61, passesNear, Bad Kreuznach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Kreuznach Context triple: [Autobahn A61, passesNear, Bad Kreuznach]
-
A.
Bad Kreuznach
chosen
Bad Kreuznach is a historic spa town in western Germany known for its saline springs, medieval architecture, and picturesque location along the Nahe River.
-
B.
Bad Schwalbach
Bad Schwalbach is a spa town in the German state of Hesse, known for its mineral springs and location in the Taunus mountains.
-
C.
Bad Wurzach
Bad Wurzach is a spa town in the Allgäu region of southern Germany, known for its moorland landscapes and therapeutic mud baths.
-
D.
Bad Cannstatt
Bad Cannstatt is a historic district of Stuttgart, Germany, known for its mineral springs, traditional architecture, and the Cannstatter Volksfest beer festival.
-
E.
Geisenheim
Geisenheim is a German town in the Rheingau wine region, known for its viticulture, wine production, and renowned university of applied sciences for wine and horticulture.
- 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dec419788190a999a68f32fab39b |
completed | April 19, 2026, 1:55 p.m. |
Created at: April 10, 2026, 10:30 a.m.