Triple
T1624666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Gera |
E35113
|
entity |
| Predicate | locatedOnRiver |
P165
|
FINISHED |
| Object | White Elster |
E119578
|
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: White Elster | Statement: [City of Gera, locatedOnRiver, White Elster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: White Elster Context triple: [City of Gera, locatedOnRiver, White Elster]
-
A.
White Elster
chosen
White Elster is a river in central Europe that flows through parts of Germany, including the city of Gera, before joining the Saale River.
-
B.
Blaustein
Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
-
C.
Ernst
Ernst is a masculine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
-
D.
Van Brunt
Van Brunt is the surname of Brom Bones, the boisterous rival of Ichabod Crane in Washington Irving’s “The Legend of Sleepy Hollow.”
-
E.
Menzel
Menzel is the surname of Idina Menzel, the American actress and singer best known for her roles in Broadway musicals and the film "Frozen."
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a909d0586c81909e399b636e130ff5 |
completed | March 5, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58ccc80c819088ecd91f0a99a247 |
completed | March 8, 2026, 11:09 a.m. |
Created at: March 4, 2026, 7:28 p.m.