Triple
T3378732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Second Coming |
E71128
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Mark Benton
Mark Benton is an English character actor known for his extensive work in British television drama and comedy, as well as stage and film roles.
|
E354410
|
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 Benton | Statement: [The Second Coming, starring, Mark Benton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Benton Context triple: [The Second Coming, starring, Mark Benton]
-
A.
Mark Suter
Mark Suter is a percussionist known for his work in contemporary and world music, including performances with the Silk Road Ensemble.
-
B.
Chris Gill
Chris Gill is a British film editor best known for his work on the acclaimed horror film "28 Days Later" and other notable UK productions.
-
C.
Brent Eleigh
Brent Eleigh is a small rural village located in the county of Suffolk in eastern England.
-
D.
Sean Barton
Sean Barton is a film editor known for his work on various feature films, including the drama "Tea with Mussolini."
-
E.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
- 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 Benton Triple: [The Second Coming, starring, Mark Benton]
Generated description
Mark Benton is an English character actor known for his extensive work in British television drama and comedy, as well as stage and film roles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Benton Target entity description: Mark Benton is an English character actor known for his extensive work in British television drama and comedy, as well as stage and film roles.
-
A.
Mark Suter
Mark Suter is a percussionist known for his work in contemporary and world music, including performances with the Silk Road Ensemble.
-
B.
Chris Gill
Chris Gill is a British film editor best known for his work on the acclaimed horror film "28 Days Later" and other notable UK productions.
-
C.
Brent Eleigh
Brent Eleigh is a small rural village located in the county of Suffolk in eastern England.
-
D.
Sean Barton
Sean Barton is a film editor known for his work on various feature films, including the drama "Tea with Mussolini."
-
E.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
- 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_69ad85a7f80c8190a05e43013f298942 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2eacb5c81908071a1dacc9a897a |
completed | March 8, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bc17dd881908fc5fb0d4a23f40f |
completed | March 12, 2026, 11:26 p.m. |
| NEDg | Description generation | batch_69b34e45a6c08190a0011eaa60f3d50a |
completed | March 12, 2026, 11:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b34eba517881908806b1ac285448ff |
completed | March 12, 2026, 11:39 p.m. |
Created at: March 8, 2026, 3:14 p.m.