Triple
T11422777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Würm River |
E270665
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Würm |
E333214
|
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: Würm | Statement: [Würm River, hasNameInLanguage, Würm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Würm Context triple: [Würm River, hasNameInLanguage, Würm]
-
A.
Würm
chosen
Würm is a river in Bavaria, Germany, known for flowing from Lake Starnberg through several towns before joining the Isar River near Munich.
-
B.
Weiswampach
Weiswampach is a small commune and village in northern Luxembourg known for its rural landscape and proximity to the Belgian border.
-
C.
Zwenkau
Zwenkau is a small town in the Free State of Saxony in eastern Germany, situated near Leipzig and known for its proximity to former lignite mining areas now being transformed into lake landscapes.
-
D.
Würges
Würges is a district of the spa town Bad Camberg in the Limburg-Weilburg region of Hesse, Germany.
-
E.
Wurmberg
Wurmberg is a prominent mountain in the Harz range of central Germany, popular for skiing, hiking, and panoramic views.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d801b357e88190ace56d36a945688f |
completed | April 9, 2026, 7:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b8a1e88c8190994bea88a0490e60 |
completed | April 20, 2026, 5:24 a.m. |
Created at: April 8, 2026, 9:34 p.m.