Triple
T13406512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lippe-Biesterfeld |
E319973
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Biesterfeld |
E673219
|
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: Biesterfeld | Statement: [Lippe-Biesterfeld, namedAfter, Biesterfeld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biesterfeld Context triple: [Lippe-Biesterfeld, namedAfter, Biesterfeld]
-
A.
Biesterfeld
chosen
Biesterfeld was a historic estate in the Principality of Lippe that served as the ancestral seat of the Lippe-Biesterfeld noble line.
-
B.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
-
C.
Faulbach
Faulbach is a district (Ortsteil) of the town of Hadamar in the Limburg-Weilburg district of Hesse, Germany.
-
D.
Neudorf
Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
-
E.
Dierdorf
Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbae4be498819094798473a1bfd853 |
completed | April 12, 2026, 2:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7461b0dd481908719fc4657bb92bb |
completed | May 3, 2026, 12:56 p.m. |
Created at: April 9, 2026, 9:35 p.m.