Triple
T9110988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cars 2 |
E218598
|
entity |
| Predicate | voiceCastMember |
P9616
|
FINISHED |
| Object |
Michel Michelis
Michel Michelis is a voice actor known for his work in the animated film "Cars 2."
|
E779032
|
NE FINISHED |
How this triple was built (4 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: Michel Michelis | Statement: [Cars 2, voiceCastMember, Michel Michelis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michel Michelis Context triple: [Cars 2, voiceCastMember, Michel Michelis]
-
A.
Michel Kelber
Michel Kelber was a French cinematographer known for his work on numerous European films from the 1930s through the 1970s.
-
B.
Jean-Michel Reusser
Jean-Michel Reusser is a film producer known for his work on the movie "I'm Your Man."
-
C.
Pierre Michel
Pierre Michel is a minor but pivotal character in Agatha Christie's detective novel "Murder on the Orient Express," serving as the train's conductor and playing a key role in the mystery surrounding the central crime.
-
D.
Bruno Beger
Bruno Beger was a German SS anthropologist and war criminal involved in Nazi racial research and atrocities during World War II.
-
E.
Philippe Knoche
Philippe Knoche is a French business executive best known for leading the nuclear energy group Areva through a major restructuring of France’s atomic industry.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Michel Michelis Triple: [Cars 2, voiceCastMember, Michel Michelis]
Generated description
Michel Michelis is a voice actor known for his work in the animated film "Cars 2."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michel Michelis Target entity description: Michel Michelis is a voice actor known for his work in the animated film "Cars 2."
-
A.
Michel Kelber
Michel Kelber was a French cinematographer known for his work on numerous European films from the 1930s through the 1970s.
-
B.
Jean-Michel Reusser
Jean-Michel Reusser is a film producer known for his work on the movie "I'm Your Man."
-
C.
Pierre Michel
Pierre Michel is a minor but pivotal character in Agatha Christie's detective novel "Murder on the Orient Express," serving as the train's conductor and playing a key role in the mystery surrounding the central crime.
-
D.
Bruno Beger
Bruno Beger was a German SS anthropologist and war criminal involved in Nazi racial research and atrocities during World War II.
-
E.
Philippe Knoche
Philippe Knoche is a French business executive best known for leading the nuclear energy group Areva through a major restructuring of France’s atomic industry.
- F. None of above. chosen
Provenance (5 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca847102881908f9d86ce9883fb1a |
completed | April 1, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d030467b188190a6d99bf2fc65207d |
completed | April 3, 2026, 9:25 p.m. |
| NEDg | Description generation | batch_69d0316f47c88190920843b469d15069 |
completed | April 3, 2026, 9:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d032778adc8190a087497507a6e1ca |
completed | April 3, 2026, 9:34 p.m. |
Created at: March 30, 2026, 7:16 p.m.