Triple
T15204237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Elster |
E363347
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Gera |
E22181
|
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: Gera | Statement: [White Elster, flowsThrough, Gera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gera Context triple: [White Elster, flowsThrough, Gera]
-
A.
Gera
chosen
Gera is a city in the German state of Thuringia, known for its industrial heritage and historic architecture along the White Elster river.
-
B.
Gera
Gera is a biblical figure mentioned in the Hebrew Bible, known primarily as a Benjamite ancestor in the genealogy of the tribe of Benjamin.
-
C.
Gera
Gera is a river in Thuringia, Germany, that flows through the city of Erfurt before joining the Unstrut.
-
D.
Gera River
The Gera River is a waterway in central Germany that flows through the city of Erfurt in the state of Thuringia.
-
E.
Eschwege
Eschwege is a small historic town in the German state of Hesse, known for its medieval architecture and location near the Werra River.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b693a48190a6230b7b52bc8cd3 |
completed | April 15, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed33b911c8190815341a342a8d3c8 |
completed | May 9, 2026, 6:24 a.m. |
Created at: April 10, 2026, 3:11 a.m.