Triple
T17744010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outsiders |
E442935
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Mark Jeffrey Miller
Mark Jeffrey Miller is an actor known for his role in the television series "Outsiders."
|
E1286340
|
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: Mark Jeffrey Miller | Statement: [Outsiders, portrayedBy, Mark Jeffrey Miller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Jeffrey Miller Context triple: [Outsiders, portrayedBy, Mark Jeffrey Miller]
-
A.
Matt Miller
Matt Miller is a television writer and producer best known for creating the comedy series "Trial & Error."
-
B.
Todd Joseph Miller
Todd Joseph Miller is an American actor and stand-up comedian known for his roles in projects like "Silicon Valley," "Deadpool," and various voice performances in animated films.
-
C.
Jim Miller
Jim Miller is an American mixed martial artist and longtime UFC lightweight contender known for his durability, grappling skills, and record number of UFC appearances.
-
D.
Jim Miller
Jim Miller is a film editor known for his work on major Hollywood productions, including the science-fiction comedy "Men in Black."
-
E.
Mark Miller
Mark Miller is an American actor and screenwriter known for his work in film and television from the mid-20th century onward.
- 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: Mark Jeffrey Miller Triple: [Outsiders, portrayedBy, Mark Jeffrey Miller]
Generated description
Mark Jeffrey Miller is an actor known for his role in the television series "Outsiders."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Jeffrey Miller Target entity description: Mark Jeffrey Miller is an actor known for his role in the television series "Outsiders."
-
A.
Matt Miller
Matt Miller is a television writer and producer best known for creating the comedy series "Trial & Error."
-
B.
Todd Joseph Miller
Todd Joseph Miller is an American actor and stand-up comedian known for his roles in projects like "Silicon Valley," "Deadpool," and various voice performances in animated films.
-
C.
Jim Miller
Jim Miller is an American mixed martial artist and longtime UFC lightweight contender known for his durability, grappling skills, and record number of UFC appearances.
-
D.
Jim Miller
Jim Miller is a film editor known for his work on major Hollywood productions, including the science-fiction comedy "Men in Black."
-
E.
Mark Miller
Mark Miller is an American actor and screenwriter known for his work in film and television from the mid-20th century onward.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47acf44f8819089a18193e37d112c |
completed | April 19, 2026, 6:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02efb26bd88190a07e9891f70559c8 |
completed | May 12, 2026, 9:15 a.m. |
| NEDg | Description generation | batch_6a02f13826388190843c9844011b5824 |
completed | May 12, 2026, 9:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02f1a8e3d4819096050f66a4e0563b |
completed | May 12, 2026, 9:23 a.m. |
Created at: April 10, 2026, 10:09 a.m.