Triple
T6869112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flachgau |
E158492
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Tennengau |
E451553
|
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: Tennengau | Statement: [Flachgau, borderedBy, Tennengau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tennengau Context triple: [Flachgau, borderedBy, Tennengau]
-
A.
Tennengau
chosen
Tennengau is a district in the Austrian state of Salzburg known for its alpine landscapes, historic salt mining heritage, and proximity to the city of Salzburg.
-
B.
Kraichgau
Kraichgau is a hilly, fertile region in southwestern Germany known for its agriculture, vineyards, and picturesque landscapes between the Black Forest and the Odenwald.
-
C.
Steigerwald
Steigerwald is a forested hill range and nature area in northern Bavaria, Germany, known for its beech forests, vineyards, and traditional Franconian landscapes.
-
D.
Naunhof
Naunhof is a small town in the Free State of Saxony in eastern Germany, known for its surrounding lakes and forests near the city of Leipzig.
-
E.
Vogelthal
Vogelthal is a small village in Bavaria, Germany, known as the birthplace of World War II tank commander Michael Wittmann.
- 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_69c68831e3648190a643c328122e4d43 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8a916a88190b81551731dff2898 |
completed | March 27, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c74299ae148190a56c7b1ee8829f40 |
completed | March 28, 2026, 2:53 a.m. |
Created at: March 27, 2026, 2:22 p.m.