Triple
T11222069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rottal-Inn |
E265593
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Tann |
E282895
|
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: Tann | Statement: [Rottal-Inn, contains, Tann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tann Context triple: [Rottal-Inn, contains, Tann]
-
A.
Tann
Tann is a small municipality in Bavaria, Germany, known for its rural character within the district of Altötting.
-
B.
Tann
chosen
Tann is a small municipality in the Rottal-Inn district of Bavaria, Germany, known for its rural character and proximity to the town of Simbach am Inn.
-
C.
Tannat
Tannat is a robust, tannin-rich red wine grape variety traditionally associated with southwestern France and now also widely grown in Uruguay and other New World regions.
-
D.
Tannay
Tannay is a small lakeside municipality in the canton of Vaud in western Switzerland, situated on the shores of Lake Geneva.
-
E.
Tanneron
Tanneron is a commune in southeastern France’s Var department, known for its hilly landscapes and extensive mimosa forests overlooking the Mediterranean hinterland.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8ec8fb08190b27144ab65f85957 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4977cab4481909c6b94ca07cd5e4a |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 8, 2026, 9:30 p.m.