Triple
T16148112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Why Women Kill |
E391838
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object |
Mark Grossan
Mark Grossan is a television producer best known for his executive production work on the darkly comedic drama series "Why Women Kill."
|
E1197898
|
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 Grossan | Statement: [Why Women Kill, executiveProducer, Mark Grossan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Grossan Context triple: [Why Women Kill, executiveProducer, Mark Grossan]
-
A.
Ben Grosse
Ben Grosse is an American record producer and mixer known for his work on major rock and metal albums by artists such as Marilyn Manson, Sevendust, and Disturbed.
-
B.
Matthew Gross
Matthew Gross is a television producer best known for his work as an executive producer on the crime drama series "Body of Proof."
-
C.
Matthew Gross
Matthew Gross is a television and film producer best known for his work on the ABC drama series "Dirty Sexy Money."
-
D.
Paul Groesse
Paul Groesse was an Academy Award–winning Hollywood art director known for his work on classic mid-20th-century films.
-
E.
J.P. Grosse
J.P. Grosse is a character from The Muppets franchise, known as Scooter’s gruff and business-minded uncle who owns the theater.
- 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 Grossan Triple: [Why Women Kill, executiveProducer, Mark Grossan]
Generated description
Mark Grossan is a television producer best known for his executive production work on the darkly comedic drama series "Why Women Kill."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Grossan Target entity description: Mark Grossan is a television producer best known for his executive production work on the darkly comedic drama series "Why Women Kill."
-
A.
Ben Grosse
Ben Grosse is an American record producer and mixer known for his work on major rock and metal albums by artists such as Marilyn Manson, Sevendust, and Disturbed.
-
B.
Matthew Gross
Matthew Gross is a television producer best known for his work as an executive producer on the crime drama series "Body of Proof."
-
C.
Matthew Gross
Matthew Gross is a television and film producer best known for his work on the ABC drama series "Dirty Sexy Money."
-
D.
Paul Groesse
Paul Groesse was an Academy Award–winning Hollywood art director known for his work on classic mid-20th-century films.
-
E.
J.P. Grosse
J.P. Grosse is a character from The Muppets franchise, known as Scooter’s gruff and business-minded uncle who owns the theater.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d9551e081908391061b092ff31b |
completed | April 17, 2026, 11:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7a9ebf08190aa21cdff051f4ba2 |
completed | May 10, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_69fff86a556c819096bc008e1ca76e8c |
completed | May 10, 2026, 3:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff926120081909f1042bf3a16ea10 |
completed | May 10, 2026, 3:19 a.m. |
Created at: April 10, 2026, 5:01 a.m.