Triple
T17407517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laichingen cave |
E423257
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Laichingen |
—
|
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: Laichingen | Statement: [Laichingen cave, near, Laichingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laichingen Context triple: [Laichingen cave, near, Laichingen]
-
A.
Laichingen
chosen
Laichingen is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its location on the Swabian Jura plateau and its historic textile industry.
-
B.
Wechingen
Wechingen is a small rural municipality in the Bavarian region of southern Germany.
-
C.
Gechingen
Gechingen is a small municipality in the German state of Baden-Württemberg, situated in the northern Black Forest region.
-
D.
Gernsbach
Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
-
E.
Riedlingen
Riedlingen is a small historic town in the state of Baden-Württemberg in southern Germany, known for its well-preserved medieval old town on the Danube River.
- 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_69d889d7d27c819088486ce3f0627fa1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43b081ae08190b144e333a3a74b02 |
completed | April 19, 2026, 2:16 a.m. |
Created at: April 10, 2026, 5:46 a.m.