Triple
T20421166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christoph Oberhauser |
E500852
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Christoph |
—
|
NE NERFINISHED |
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: Christoph | Statement: [Christoph Oberhauser, givenName, Christoph]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christoph Context triple: [Christoph Oberhauser, givenName, Christoph]
-
A.
Christoph
chosen
Christoph is the given name of Christoph Willibald Gluck, the influential 18th-century composer known for reforming opera.
-
B.
Wolfgang
Wolfgang is a recurring villain and boss character in the Skylanders video game series, known for his werewolf-like appearance and musical, sound-based attacks.
-
C.
Wolfgang
Wolfgang is the given name of Johann Wolfgang von Goethe, the renowned German writer, poet, and statesman.
-
D.
Johann
Johann is a given name of Germanic origin commonly used in German-speaking and other European countries.
-
E.
Philipp Moritz
Philipp Moritz is a researcher in machine learning and reinforcement learning, known for co-authoring influential work such as the Proximal Policy Optimization (PPO) algorithm.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4aa68fc8190b1a14c55575ef04a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67ba479008190bf6d31ee79f3a401 |
completed | April 20, 2026, 7:16 p.m. |
Created at: April 16, 2026, 11:30 a.m.