Triple
T3205042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Why Did I Get Married? |
E67140
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Denise Boutte
Denise Boutte is an American actress and model best known for her roles in Tyler Perry’s films and television projects.
|
E335268
|
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: Denise Boutte | Statement: [Why Did I Get Married?, stars, Denise Boutte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Denise Boutte Context triple: [Why Did I Get Married?, stars, Denise Boutte]
-
A.
Alicia Landry
Alicia Landry is the widow of legendary Dallas Cowboys head coach Tom Landry and was known for her active role in charitable and community work alongside him.
-
B.
LaVonne Griffin-Valade
LaVonne Griffin-Valade is an American public official and former auditor who serves as Oregon’s Secretary of State.
-
C.
Marie Dionne Warrick
Marie Dionne Warrick, better known as Dionne Warwick, is an American singer and actress renowned for her soulful pop and R&B hits and long collaboration with songwriters Burt Bacharach and Hal David.
-
D.
Lorraine Miller
Lorraine Miller was an American actress and dancer active in Hollywood films during the 1940s and 1950s.
-
E.
Larissa Weems
Larissa Weems is a character from the Netflix series "Wednesday," serving as the poised and enigmatic principal of Nevermore Academy.
- 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: Denise Boutte Triple: [Why Did I Get Married?, stars, Denise Boutte]
Generated description
Denise Boutte is an American actress and model best known for her roles in Tyler Perry’s films and television projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Denise Boutte Target entity description: Denise Boutte is an American actress and model best known for her roles in Tyler Perry’s films and television projects.
-
A.
Alicia Landry
Alicia Landry is the widow of legendary Dallas Cowboys head coach Tom Landry and was known for her active role in charitable and community work alongside him.
-
B.
LaVonne Griffin-Valade
LaVonne Griffin-Valade is an American public official and former auditor who serves as Oregon’s Secretary of State.
-
C.
Marie Dionne Warrick
Marie Dionne Warrick, better known as Dionne Warwick, is an American singer and actress renowned for her soulful pop and R&B hits and long collaboration with songwriters Burt Bacharach and Hal David.
-
D.
Lorraine Miller
Lorraine Miller was an American actress and dancer active in Hollywood films during the 1940s and 1950s.
-
E.
Larissa Weems
Larissa Weems is a character from the Netflix series "Wednesday," serving as the poised and enigmatic principal of Nevermore Academy.
- 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_69ad8589bd988190afa7ed2bdffb7b33 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaa559848819082d1e61f586278dd |
completed | March 8, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24bcbb0e88190b4413c4ba3de0eeb |
completed | March 12, 2026, 5:14 a.m. |
| NEDg | Description generation | batch_69b24ca05434819080ee515b1e7bdcb4 |
completed | March 12, 2026, 5:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b24d19250c81908a9c3ac95b83a473 |
completed | March 12, 2026, 5:20 a.m. |
Created at: March 8, 2026, 3:07 p.m.