Triple
T11528079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knecht Ruprecht |
E273349
|
entity |
| Predicate | meaningOfName |
P1966
|
FINISHED |
| Object | Servant Rupert |
E74177
|
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: Servant Rupert | Statement: [Knecht Ruprecht, meaningOfName, Servant Rupert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Servant Rupert Context triple: [Knecht Ruprecht, meaningOfName, Servant Rupert]
-
A.
Rupert
chosen
Rupert is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by various notable figures.
-
B.
Rupert
Rupert is a small town located in Greenbrier County in the state of West Virginia, United States.
-
C.
Rupert
Rupert is a small agricultural city in south-central Idaho known for its historic town square and role as a local farming hub.
-
D.
Lucan the Butler
Lucan the Butler is a knight of the Round Table in Arthurian legend, known for his loyalty and service to King Arthur.
-
E.
Baldrick
Baldrick is a dim-witted yet loyal servant best known from the British historical sitcom "Blackadder," famous for his disastrously bad "cunning plans."
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d883972b10819093bd09cf8406671c |
completed | April 10, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6256e3130819095a93c8d1891b078 |
completed | April 20, 2026, 1:09 p.m. |
Created at: April 8, 2026, 9:37 p.m.