Triple
T10968531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waldeck-Frankenberg |
E259169
|
entity |
| Predicate | hasRiver |
P165
|
FINISHED |
| Object | Twiste |
E888725
|
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: Twiste | Statement: [Waldeck-Frankenberg, hasRiver, Twiste]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Twiste Context triple: [Waldeck-Frankenberg, hasRiver, Twiste]
-
A.
Twiste
chosen
Twiste is a river in central Germany that serves as a tributary of the Diemel, flowing through the states of Hesse and North Rhine-Westphalia.
-
B.
Gilot
Gilot is a French surname most notably borne by Françoise Gilot, the painter and writer known for her long relationship with Pablo Picasso.
-
C.
Taaffe
Taaffe is a surname of Irish origin borne by various notable individuals across fields such as politics, the arts, and academia.
-
D.
Mistinguett
Mistinguett was a famous French actress and singer of the early 20th century, celebrated as one of Paris’s most iconic music-hall stars.
-
E.
Schultheiss
Schultheiss is a German surname historically derived from a medieval administrative title for a local official or magistrate.
- 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7719800388190943a0bffa48a2731 |
completed | April 9, 2026, 9:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2d78156c48190a956dc22b9832bcb |
completed | April 18, 2026, 12:59 a.m. |
Created at: April 8, 2026, 9:24 p.m.