Triple
T4742086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Count of Nassau-Siegen |
E105267
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Nassau-Siegen |
E457423
|
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: Nassau-Siegen | Statement: [Count of Nassau-Siegen, namedAfter, Nassau-Siegen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nassau-Siegen Context triple: [Count of Nassau-Siegen, namedAfter, Nassau-Siegen]
-
A.
Nassau-Siegen
chosen
Nassau-Siegen was a German county in the Holy Roman Empire centered on the town of Siegen and ruled by a branch of the House of Nassau.
-
B.
Nassau-Weilburg
Nassau-Weilburg was a historical German county and later principality within the Holy Roman Empire, ruled by a branch of the House of Nassau.
-
C.
Badenburg
Badenburg is an ornate pavilion within Munich’s Nymphenburg Palace park, known for its richly decorated interiors and historical bathing hall.
-
D.
Stolberg
Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
-
E.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
- 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_69bd43ef87a48190a5bc3600711aa032 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64a7153881909eac451fc7566d25 |
completed | March 20, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be3a28ca648190a44d178826926812 |
completed | March 21, 2026, 6:26 a.m. |
Created at: March 20, 2026, 1:19 p.m.