Triple
T5173552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Cross |
E116740
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Tobias Fünke |
E134421
|
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: Tobias Fünke | Statement: [David Cross, notableWork, Tobias Fünke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tobias Fünke Context triple: [David Cross, notableWork, Tobias Fünke]
-
A.
Tobias Fünke
chosen
Tobias Fünke is a socially awkward, aspiring actor and former analyst-therapist known for his oblivious behavior and unintentional double entendres in the television series "Arrested Development."
-
B.
Tobias Kohn
Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
-
C.
Markus Vogt
Markus Vogt is an architect known for his work on the design of the Bundesplatz in Switzerland.
-
D.
Tobias Ritter
Tobias Ritter is a German-born organic chemist recognized for his contributions to fluorination chemistry and for leading a prominent research group in synthetic methodology.
-
E.
Christoph Dolle
Christoph Dolle is a German local politician who serves as the mayor of the town of Blomberg.
- 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_69bd445ff97c81909a2615cc56235470 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd796f7c308190a721e33aabd499ac |
completed | March 20, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf06ac60448190a2e97a4df03863ea |
completed | March 21, 2026, 8:59 p.m. |
Created at: March 20, 2026, 1:45 p.m.