Triple
T17947150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ira Steven Behr |
E448731
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Laura Behr
Laura Behr is the wife of American television producer and writer Ira Steven Behr, known for his work on Star Trek: Deep Space Nine.
|
E1297515
|
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: Laura Behr | Statement: [Ira Steven Behr, spouse, Laura Behr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Behr Context triple: [Ira Steven Behr, spouse, Laura Behr]
-
A.
Felicia Minei Behr
Felicia Minei Behr is an American television producer best known for her influential work in daytime soap operas.
-
B.
Alexandra Behrs
Alexandra Behrs was a member of the Behrs family and a sister of Sofya Andreyevna Behrs, the wife of Russian writer Leo Tolstoy.
-
C.
Laura Bickford
Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
-
D.
Betsy Rue
Betsy Rue is an American actress best known for her roles in horror and thriller films, including her appearance in the slasher movie "My Bloody Valentine 3D."
-
E.
Laura Rister
Laura Rister is a film producer and executive known for her work on independent and prestige projects, including the financial thriller "Margin Call."
- 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: Laura Behr Triple: [Ira Steven Behr, spouse, Laura Behr]
Generated description
Laura Behr is the wife of American television producer and writer Ira Steven Behr, known for his work on Star Trek: Deep Space Nine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laura Behr Target entity description: Laura Behr is the wife of American television producer and writer Ira Steven Behr, known for his work on Star Trek: Deep Space Nine.
-
A.
Felicia Minei Behr
Felicia Minei Behr is an American television producer best known for her influential work in daytime soap operas.
-
B.
Alexandra Behrs
Alexandra Behrs was a member of the Behrs family and a sister of Sofya Andreyevna Behrs, the wife of Russian writer Leo Tolstoy.
-
C.
Laura Bickford
Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
-
D.
Betsy Rue
Betsy Rue is an American actress best known for her roles in horror and thriller films, including her appearance in the slasher movie "My Bloody Valentine 3D."
-
E.
Laura Rister
Laura Rister is a film producer and executive known for her work on independent and prestige projects, including the financial thriller "Margin Call."
- 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_69d8b9f8cca8819099836916c56b7c95 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4ad99df408190a8a4e3d21c03fe71 |
completed | April 19, 2026, 10:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03291c5e008190981dfef6aad81054 |
completed | May 12, 2026, 1:20 p.m. |
| NEDg | Description generation | batch_6a032ae7f4808190ade6b20198aadcba |
completed | May 12, 2026, 1:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a032b6033b48190b889bd4d98a58ea2 |
completed | May 12, 2026, 1:30 p.m. |
Created at: April 10, 2026, 10:21 a.m.