Triple
T14520207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Mule |
E340629
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Nick Schenk |
E529155
|
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: Nick Schenk | Statement: [The Mule, screenwriter, Nick Schenk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nick Schenk Context triple: [The Mule, screenwriter, Nick Schenk]
-
A.
Nick Schenk
chosen
Nick Schenk is an American screenwriter best known for writing the Clint Eastwood film "Gran Torino."
-
B.
Jon Schneck
Jon Schneck is an American guitarist and multi-instrumentalist best known for his work with the Christian rock band Relient K.
-
C.
Dick Schoof
Dick Schoof is a Dutch civil servant and politician who has served as Prime Minister of the Netherlands.
-
D.
Alan Schilke
Alan Schilke is a prominent roller coaster engineer known for designing innovative and extreme thrill rides for major amusement parks worldwide.
-
E.
Tom Schaul
Tom Schaul is a machine learning researcher known for his contributions to deep reinforcement learning, including co-developing the Dueling DQN architecture.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de9a70b15c81908773633e989ef704 |
completed | April 14, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda90e1778819095f5ac8848120098 |
completed | May 8, 2026, 9:12 a.m. |
Created at: April 10, 2026, 1:22 a.m.