Triple
T20466867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tittmoning |
E502073
|
entity |
| Predicate | nearbyTown |
P3883
|
FINISHED |
| Object | Burghausen |
—
|
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: Burghausen | Statement: [Tittmoning, nearbyTown, Burghausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burghausen Context triple: [Tittmoning, nearbyTown, Burghausen]
-
A.
Burghausen
chosen
Burghausen is a historic Bavarian town in southeastern Germany, renowned for its remarkably well-preserved medieval old town and one of the longest castle complexes in the world.
-
B.
Haßfurt
Haßfurt is a small town in northern Bavaria, Germany, situated on the Main River and known for its historic architecture and regional administrative role.
-
C.
Traunstein
Traunstein is a prominent limestone mountain in the Salzkammergut region of Upper Austria, overlooking Lake Traunsee and popular for hiking and climbing.
-
D.
Traunstein
Traunstein is a town in southeastern Bavaria, Germany, known as a regional administrative and cultural center near the Chiemsee and the Alps.
-
E.
Forchheim
Forchheim is a town in Upper Franconia, Bavaria, Germany, known for its historic old town and location along major regional rail and road routes.
- 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_69e0b4ae5f1081908768b0c9a3a0bf38 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6995d9d1c81909ee223a35a0850ba |
completed | April 20, 2026, 9:23 p.m. |
Created at: April 16, 2026, 11:33 a.m.