Triple
T2582977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince of Waldeck |
E57134
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Waldeck |
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 | Statement: [Prince of Waldeck, region, Waldeck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waldeck Context triple: [Prince of Waldeck, region, Waldeck]
-
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.
Jacques Ignace Hittorff
Jacques Ignace Hittorff was a 19th-century German-born French architect and urban planner known for his major contributions to Parisian architecture and city design, including prominent public buildings and squares.
-
D.
Jean-Frédéric Waldeck
Jean-Frédéric Waldeck was a 19th-century French explorer and artist known for his early, often romanticized and inaccurate, depictions and studies of Maya ruins in Mexico.
-
E.
Ervin
Ervin is a masculine given name of Germanic origin, closely related to names like Erwin and Irvin.
- 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_69af657f39dc8190971e0ad7a5396257 |
completed | March 10, 2026, 12:27 a.m. |
Created at: March 6, 2026, 9:49 p.m.