Triple
T3820234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mars (TV series) |
E84352
|
entity |
| Predicate | mainCastMember |
P5563
|
FINISHED |
| Object |
Gunnar Cauthery
Gunnar Cauthery is an actor best known for his role in the television series "Mars."
|
E391833
|
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: Gunnar Cauthery | Statement: [Mars (TV series), mainCastMember, Gunnar Cauthery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gunnar Cauthery Context triple: [Mars (TV series), mainCastMember, Gunnar Cauthery]
-
A.
Gunnar
Gunnar is a masculine given name of Old Norse origin, commonly used in Scandinavian countries and associated with warriors or bold fighters.
-
B.
Frederic Knudtson
Frederic Knudtson was an American film editor known for his work on numerous Hollywood productions in the mid-20th century.
-
C.
Ulf Danielsson
Ulf Danielsson is a Swedish theoretical physicist and cosmologist known for his work on string theory and the fundamental nature of the universe.
-
D.
Finn Arnesson
Finn Arnesson was an 11th-century Norwegian nobleman and powerful chieftain closely involved in the politics of the Norwegian and Scottish courts.
-
E.
Gunnar Berge
Gunnar Berge is a Norwegian Labour Party politician who has held several ministerial posts, including Minister of Finance, and later served as head of the Office of the Auditor General of Norway.
- 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: Gunnar Cauthery Triple: [Mars (TV series), mainCastMember, Gunnar Cauthery]
Generated description
Gunnar Cauthery is an actor best known for his role in the television series "Mars."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gunnar Cauthery Target entity description: Gunnar Cauthery is an actor best known for his role in the television series "Mars."
-
A.
Gunnar
Gunnar is a masculine given name of Old Norse origin, commonly used in Scandinavian countries and associated with warriors or bold fighters.
-
B.
Frederic Knudtson
Frederic Knudtson was an American film editor known for his work on numerous Hollywood productions in the mid-20th century.
-
C.
Ulf Danielsson
Ulf Danielsson is a Swedish theoretical physicist and cosmologist known for his work on string theory and the fundamental nature of the universe.
-
D.
Finn Arnesson
Finn Arnesson was an 11th-century Norwegian nobleman and powerful chieftain closely involved in the politics of the Norwegian and Scottish courts.
-
E.
Gunnar Berge
Gunnar Berge is a Norwegian Labour Party politician who has held several ministerial posts, including Minister of Finance, and later served as head of the Office of the Auditor General of Norway.
- 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_69aed931f5908190be2c07af66d4df25 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeea61a63c819086e16b89d2ea2157 |
completed | March 9, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb4998248190b4174dd80a8e790c |
completed | March 14, 2026, 6:08 a.m. |
| NEDg | Description generation | batch_69b4ffcf7e24819098cf2e46b92bed4a |
completed | March 14, 2026, 6:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b500596e308190a31e44c24de3f31d |
completed | March 14, 2026, 6:29 a.m. |
Created at: March 9, 2026, 3:17 p.m.