Triple
T21748583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District of Coburg |
E536850
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Ahorn |
—
|
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: Ahorn | Statement: [District of Coburg, hasMunicipality, Ahorn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ahorn Context triple: [District of Coburg, hasMunicipality, Ahorn]
-
A.
Ahorn
chosen
Ahorn is a municipality in the Bavarian region of Germany, known for its rural character and proximity to the city of Coburg.
-
B.
Birch
Birch is a masculine given name most notably borne by American politician Birch Bayh, a long-serving U.S. senator from Indiana.
-
C.
Nyssa
Nyssa is a companion of the Fifth Doctor in the long-running British science fiction television series Doctor Who.
-
D.
Nyssa
Nyssa is a small genus of deciduous trees native to North America and East Asia, commonly known as tupelos or gum trees, valued for their attractive foliage and ecological importance.
-
E.
Beech
Beech is a small rural village and civil parish in East Hampshire, England, known for its wooded surroundings and residential character.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a77e19c81909bf26f96aa41a7ce |
completed | April 28, 2026, 2:24 a.m. |
Created at: April 16, 2026, 6:50 p.m.