Triple
T9495374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trave |
E228990
|
entity |
| Predicate | hasMouthIn |
P1008
|
FINISHED |
| Object | Travemünde |
E332834
|
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: Travemünde | Statement: [Trave, hasMouthIn, Travemünde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Travemünde Context triple: [Trave, hasMouthIn, Travemünde]
-
A.
Travemünde
chosen
Travemünde is a Baltic Sea resort town and seaside district of Lübeck in northern Germany, known for its beaches, harbor, and maritime tourism.
-
B.
Bremerhaven
Bremerhaven is a major German port city on the North Sea, known for its maritime industry, shipbuilding, and role as a key hub for trade and logistics.
-
C.
Itzehoe
Itzehoe is a historic town in northern Germany known for its medieval origins and role as a regional center in the state of Schleswig-Holstein.
-
D.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
E.
Wismar
Wismar is a historic Hanseatic port city on Germany’s Baltic Sea coast, known for its well-preserved medieval architecture and UNESCO-listed old town.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd95eb87b081908fc7255598cd9a24 |
completed | April 1, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1820ba67881909955c4198c7289b1 |
completed | April 4, 2026, 9:26 p.m. |
Created at: March 30, 2026, 7:56 p.m.