Triple
T15211048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Banks |
E363514
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Amy Baer
Amy Baer is an American film producer and former studio executive known for developing and producing a range of mainstream and independent movies.
|
E1144087
|
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: Amy Baer | Statement: [Brian Banks, producer, Amy Baer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amy Baer Context triple: [Brian Banks, producer, Amy Baer]
-
A.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
B.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
C.
Belinda Keaton
Belinda Keaton is the central protagonist of the work "Night Shift," around whom the main events and character dynamics revolve.
-
D.
Barbara Pepper
Barbara Pepper was an American film and television actress best known for her comedic roles in the mid-20th century, including a regular part on the sitcom "Green Acres."
-
E.
Jane Froman
Jane Froman was an American singer and actress popular from the 1930s to the 1950s, known for her radio, stage, and film performances as well as her resilience after surviving a devastating plane crash.
- 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: Amy Baer Triple: [Brian Banks, producer, Amy Baer]
Generated description
Amy Baer is an American film producer and former studio executive known for developing and producing a range of mainstream and independent movies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amy Baer Target entity description: Amy Baer is an American film producer and former studio executive known for developing and producing a range of mainstream and independent movies.
-
A.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
B.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
C.
Belinda Keaton
Belinda Keaton is the central protagonist of the work "Night Shift," around whom the main events and character dynamics revolve.
-
D.
Barbara Pepper
Barbara Pepper was an American film and television actress best known for her comedic roles in the mid-20th century, including a regular part on the sitcom "Green Acres."
-
E.
Jane Froman
Jane Froman was an American singer and actress popular from the 1930s to the 1950s, known for her radio, stage, and film performances as well as her resilience after surviving a devastating plane crash.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076ad4ec81908d36f541fca08d72 |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed33f9abc8190bf8166c1fd9fcac6 |
completed | May 9, 2026, 6:25 a.m. |
| NEDg | Description generation | batch_69fed7b95124819097783740d9990e75 |
completed | May 9, 2026, 6:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed84f6a888190afd2d0cf2ba9d3a2 |
completed | May 9, 2026, 6:46 a.m. |
Created at: April 10, 2026, 3:11 a.m.