Triple
T3874177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All My Children |
E92456
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object |
Felicia Minei Behr
Felicia Minei Behr is an American television producer best known for her influential work in daytime soap operas.
|
E395536
|
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: Felicia Minei Behr | Statement: [All My Children, executiveProducer, Felicia Minei Behr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Felicia Minei Behr Context triple: [All My Children, executiveProducer, Felicia Minei Behr]
-
A.
Lila Schwarzenberg
Lila Schwarzenberg is an Austrian journalist, philanthropist, and former Czech countess known for her cultural and charitable work in Europe.
-
B.
Lauretta Geigerman
Lauretta Geigerman was the wife of American mob boss Frank Costello, a prominent figure in mid-20th-century organized crime.
-
C.
Hilde Benjamin
Hilde Benjamin was a prominent East German jurist and politician, notorious as a hardline judge and later Minister of Justice in the German Democratic Republic.
-
D.
Juliana Minsky
Juliana Minsky is a daughter of pioneering artificial intelligence researcher Marvin Minsky.
-
E.
Emma Berman
Emma Berman is an American voice actress best known for voicing Giulia Marcovaldo in Pixar's animated film "Luca."
- 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: Felicia Minei Behr Triple: [All My Children, executiveProducer, Felicia Minei Behr]
Generated description
Felicia Minei Behr is an American television producer best known for her influential work in daytime soap operas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Felicia Minei Behr Target entity description: Felicia Minei Behr is an American television producer best known for her influential work in daytime soap operas.
-
A.
Lila Schwarzenberg
Lila Schwarzenberg is an Austrian journalist, philanthropist, and former Czech countess known for her cultural and charitable work in Europe.
-
B.
Lauretta Geigerman
Lauretta Geigerman was the wife of American mob boss Frank Costello, a prominent figure in mid-20th-century organized crime.
-
C.
Hilde Benjamin
Hilde Benjamin was a prominent East German jurist and politician, notorious as a hardline judge and later Minister of Justice in the German Democratic Republic.
-
D.
Juliana Minsky
Juliana Minsky is a daughter of pioneering artificial intelligence researcher Marvin Minsky.
-
E.
Emma Berman
Emma Berman is an American voice actress best known for voicing Giulia Marcovaldo in Pixar's animated film "Luca."
- 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec59bea08190b1e193f34944a2ee |
completed | March 9, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5124cdf588190b3b83ee8fb29450a |
completed | March 14, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69b5132715888190bc5ba6182965e813 |
completed | March 14, 2026, 7:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b513b11dcc8190a2c2e3f27b4cf25e |
completed | March 14, 2026, 7:52 a.m. |
Created at: March 9, 2026, 3:20 p.m.