Triple
T12216082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aimee Brooks |
E291087
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Monster Man
Monster Man is a 2003 American horror-comedy film known for its blend of slasher elements and dark humor.
|
E970520
|
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: Monster Man | Statement: [Aimee Brooks, notableWork, Monster Man]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monster Man Context triple: [Aimee Brooks, notableWork, Monster Man]
-
A.
Monster
Monster is a town in the Dutch province of South Holland, known for its coastal location near the North Sea and its greenhouse horticulture.
-
B.
Monster
Monster is a popular energy drink brand known for its high-caffeine beverages and aggressive, extreme-sports-oriented marketing.
-
C.
Monster
"Monster" is a critically acclaimed Japanese manga series by Naoki Urasawa, known for its dark psychological thriller narrative about a doctor entangled with a serial killer.
-
D.
Monster
Monster is a 2003 biographical crime drama film in which Charlize Theron delivers an Oscar-winning performance as serial killer Aileen Wuornos.
-
E.
Monster
"Monster" is a standout track from Kanye West’s critically acclaimed album *My Beautiful Dark Twisted Fantasy*, known for its high-profile guest verses and dark, aggressive themes.
- 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: Monster Man Triple: [Aimee Brooks, notableWork, Monster Man]
Generated description
Monster Man is a 2003 American horror-comedy film known for its blend of slasher elements and dark humor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monster Man Target entity description: Monster Man is a 2003 American horror-comedy film known for its blend of slasher elements and dark humor.
-
A.
Monster
Monster is a town in the Dutch province of South Holland, known for its coastal location near the North Sea and its greenhouse horticulture.
-
B.
Monster
"Monster" is a critically acclaimed Japanese manga series by Naoki Urasawa, known for its dark psychological thriller narrative about a doctor entangled with a serial killer.
-
C.
Monster
Monster is a 2003 biographical crime drama film in which Charlize Theron delivers an Oscar-winning performance as serial killer Aileen Wuornos.
-
D.
Monster
Monster is a popular energy drink brand known for its high-caffeine beverages and aggressive, extreme-sports-oriented marketing.
-
E.
Monster
Monster is the first solo studio album by American rapper Killer Mike, showcasing his aggressive Southern hip hop style and politically charged lyricism.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c9419d48190b0037fe8edc681c4 |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60aa31f548190bd4f8cfe3c55614b |
completed | May 2, 2026, 2:30 p.m. |
| NEDg | Description generation | batch_69f60dbe4f788190a3b4be4b31cfbffa |
completed | May 2, 2026, 2:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60ee037bc8190be486e30e03031a7 |
completed | May 2, 2026, 2:49 p.m. |
Created at: April 8, 2026, 9:51 p.m.