Triple
T2582986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince of Waldeck |
E57134
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Waldeck-Pyrmont |
E41198
|
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: Waldeck-Pyrmont | Statement: [Prince of Waldeck, associatedWith, Waldeck-Pyrmont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waldeck-Pyrmont Context triple: [Prince of Waldeck, associatedWith, Waldeck-Pyrmont]
-
A.
Waldeck
chosen
Waldeck was a small German principality whose soldiers, like the Hessian troops, were hired out as auxiliaries to foreign powers in the 18th century.
-
B.
Minna Waldeck
Minna Waldeck was the wife of renowned German mathematician and scientist Carl Friedrich Gauss.
-
C.
Verny
Verny was the historical name of the city now known as Almaty, a major cultural and economic center in Kazakhstan.
-
D.
Gave de Pau
Gave de Pau is a river in southwestern France that flows through the city of Pau and forms part of the Adour river system in the Pyrenees region.
-
E.
Thiers
Thiers is a historic French town in the Auvergne region, renowned as a major traditional center of cutlery and knife-making.
- 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_69ab4a4dca6481908c301f8e317396e7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3c9d0548190b29743ac1d7837ff |
completed | March 7, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af83b4e09c8190909df4ba9b29d525 |
completed | March 10, 2026, 2:36 a.m. |
Created at: March 6, 2026, 9:49 p.m.