Triple
T14766151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Necessary Roughness |
E347000
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Gabrielle Pittman
Gabrielle Pittman is a fictional character appearing in the sports comedy film "Necessary Roughness."
|
E1121416
|
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: Gabrielle Pittman | Statement: [Necessary Roughness, hasCharacter, Gabrielle Pittman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gabrielle Pittman Context triple: [Necessary Roughness, hasCharacter, Gabrielle Pittman]
-
A.
Gabrielle Simpson
Gabrielle Simpson is the witty and resourceful secretary who becomes the romantic lead opposite a struggling screenwriter in the 1964 romantic comedy film "Paris When It Sizzles."
-
B.
Gabrielle Glore
Gabrielle Glore is a film producer best known for her work on the romantic drama "Sylvie’s Love."
-
C.
Karen Pittman
Karen Pittman is an American actress known for her work in television, film, and theater, including prominent roles in series like The Morning Show and And Just Like That.
-
D.
Valerie Pitts
Valerie Pitts is a British television presenter and former BBC announcer best known as the widow of renowned conductor Sir Georg Solti.
-
E.
Gabrielle Ryan
Gabrielle Ryan is a British actress known for her role in the crime drama series "Power Book IV: Force."
- 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: Gabrielle Pittman Triple: [Necessary Roughness, hasCharacter, Gabrielle Pittman]
Generated description
Gabrielle Pittman is a fictional character appearing in the sports comedy film "Necessary Roughness."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gabrielle Pittman Target entity description: Gabrielle Pittman is a fictional character appearing in the sports comedy film "Necessary Roughness."
-
A.
Gabrielle Simpson
Gabrielle Simpson is the witty and resourceful secretary who becomes the romantic lead opposite a struggling screenwriter in the 1964 romantic comedy film "Paris When It Sizzles."
-
B.
Gabrielle Glore
Gabrielle Glore is a film producer best known for her work on the romantic drama "Sylvie’s Love."
-
C.
Karen Pittman
Karen Pittman is an American actress known for her work in television, film, and theater, including prominent roles in series like The Morning Show and And Just Like That.
-
D.
Valerie Pitts
Valerie Pitts is a British television presenter and former BBC announcer best known as the widow of renowned conductor Sir Georg Solti.
-
E.
Gabrielle Ryan
Gabrielle Ryan is a British actress known for her role in the crime drama series "Power Book IV: Force."
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f576c881909da70627f5897c94 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe388aeb9c819099a987a819959479 |
completed | May 8, 2026, 7:24 p.m. |
| NEDg | Description generation | batch_69fe39320df88190b3fa197b87d78f43 |
completed | May 8, 2026, 7:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe397e0a788190bffa7d07864829d2 |
completed | May 8, 2026, 7:29 p.m. |
Created at: April 10, 2026, 1:30 a.m.