Triple
T15311920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magic Mike |
E366057
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Mike Lane
Mike Lane is the charismatic male stripper and aspiring entrepreneur portrayed by Channing Tatum in the Magic Mike film series.
|
E1150732
|
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: Mike Lane | Statement: [Magic Mike, mainCharacter, Mike Lane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Lane Context triple: [Magic Mike, mainCharacter, Mike Lane]
-
A.
Mike Lane
Mike Lane is an actor best known for his role in the 1956 boxing drama film "The Harder They Fall."
-
B.
Matt Lane
Matt Lane is a musician best known for his past role as a member of the American rock band Drive-By Truckers.
-
C.
Rob Lane
Rob Lane is a British composer best known for his work on film and television scores, including the soundtrack for "The Damned United."
-
D.
Mike Fellows
Mike Fellows is an American musician best known as a bassist and multi-instrumentalist associated with influential indie and experimental bands, including his work with Silver Jews.
-
E.
Mitch Rouse
Mitch Rouse is an American actor, comedian, and writer known for his work in film and television, including co-creating the series "Strangers with Candy" and appearing in numerous comedic roles.
- 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: Mike Lane Triple: [Magic Mike, mainCharacter, Mike Lane]
Generated description
Mike Lane is the charismatic male stripper and aspiring entrepreneur portrayed by Channing Tatum in the Magic Mike film series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mike Lane Target entity description: Mike Lane is the charismatic male stripper and aspiring entrepreneur portrayed by Channing Tatum in the Magic Mike film series.
-
A.
Mike Lane
Mike Lane is an actor best known for his role in the 1956 boxing drama film "The Harder They Fall."
-
B.
Matt Lane
Matt Lane is a musician best known for his past role as a member of the American rock band Drive-By Truckers.
-
C.
Rob Lane
Rob Lane is a British composer best known for his work on film and television scores, including the soundtrack for "The Damned United."
-
D.
Mike Fellows
Mike Fellows is an American musician best known as a bassist and multi-instrumentalist associated with influential indie and experimental bands, including his work with Silver Jews.
-
E.
Mitch Rouse
Mitch Rouse is an American actor, comedian, and writer known for his work in film and television, including co-creating the series "Strangers with Candy" and appearing in numerous comedic roles.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03cd2d5a88190aead748920f93d47 |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8a3da3881909b50cfbec0543adc |
completed | May 9, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_69fefdb82b2081908084a12a58ad3477 |
completed | May 9, 2026, 9:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fefe6c42708190bd893885fc5bc88e |
completed | May 9, 2026, 9:29 a.m. |
Created at: April 10, 2026, 3:16 a.m.