Triple
T11366589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afterburn/Aftershock |
E269221
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Caleb Hunt
Caleb Hunt is an actor known for his role in the film "Afterburn/Aftershock."
|
E922768
|
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: Caleb Hunt | Statement: [Afterburn/Aftershock, hasCastMember, Caleb Hunt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caleb Hunt Context triple: [Afterburn/Aftershock, hasCastMember, Caleb Hunt]
-
A.
Nico Parker
Nico Parker is a British actress known for her breakout role in Disney's live-action "Dumbo" (2019) and subsequent appearances in film and television.
-
B.
Joseph C. Wright
Joseph C. Wright was an American film art director known for his work on numerous Hollywood productions during the mid-20th century.
-
C.
Sacha Gervasi
Sacha Gervasi is a British screenwriter and director known for films such as "The Terminal" and the documentary "Anvil! The Story of Anvil."
-
D.
Michael Cuesta
Michael Cuesta is an American film and television director and producer known for his work on series such as Homeland, Dexter, and Six Feet Under.
-
E.
Dan Goor
Dan Goor is an American television writer and producer best known for co-creating the comedy series "Brooklyn Nine-Nine" and his work on shows like "Parks and Recreation."
- 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: Caleb Hunt Triple: [Afterburn/Aftershock, hasCastMember, Caleb Hunt]
Generated description
Caleb Hunt is an actor known for his role in the film "Afterburn/Aftershock."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Caleb Hunt Target entity description: Caleb Hunt is an actor known for his role in the film "Afterburn/Aftershock."
-
A.
Nico Parker
Nico Parker is a British actress known for her breakout role in Disney's live-action "Dumbo" (2019) and subsequent appearances in film and television.
-
B.
Joseph C. Wright
Joseph C. Wright was an American film art director known for his work on numerous Hollywood productions during the mid-20th century.
-
C.
Sacha Gervasi
Sacha Gervasi is a British screenwriter and director known for films such as "The Terminal" and the documentary "Anvil! The Story of Anvil."
-
D.
Michael Cuesta
Michael Cuesta is an American film and television director and producer known for his work on series such as Homeland, Dexter, and Six Feet Under.
-
E.
Dan Goor
Dan Goor is an American television writer and producer best known for co-creating the comedy series "Brooklyn Nine-Nine" and his work on shows like "Parks and Recreation."
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea88558c8190aa18881af51a7b96 |
completed | April 9, 2026, 6:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58bb3bd648190affa7ee85027c958 |
completed | April 20, 2026, 2:13 a.m. |
| NEDg | Description generation | batch_69e59323c5948190bc2c9512f7b0a54f |
completed | April 20, 2026, 2:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e599c53704819097c0fdbbfbbb1e87 |
completed | April 20, 2026, 3:13 a.m. |
Created at: April 8, 2026, 9:33 p.m.