Triple
T13312475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenne |
E317102
|
entity |
| Predicate | hasMajorTributary |
P415
|
FINISHED |
| Object | Nette (Lenne) |
E379323
|
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: Nette (Lenne) | Statement: [Lenne, hasMajorTributary, Nette (Lenne)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nette (Lenne) Context triple: [Lenne, hasMajorTributary, Nette (Lenne)]
-
A.
Nette
chosen
Nette is a river in Germany that serves as a tributary of the Innerste.
-
B.
Zelníčková
Zelníčková is a Czech surname, notably borne by Ivana Marie Zelníčková, the Czech-American businesswoman and former wife of Donald Trump.
-
C.
Nosková
Nosková is a Czech surname, typically the feminine form of the surname Nosek.
-
D.
Brněnec
Brněnec is a village in the Czech Republic known as the location of Oskar Schindler’s factory where many Jewish workers, later called the Schindlerjuden, were saved during the Holocaust.
-
E.
Novotný
Novotný is a common Czech surname borne by various notable figures in politics, arts, and sports.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990f6d34c8190ba19dc2df7d42c22 |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716e7b9a48190a33b04df8ad45ed8 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:29 p.m.