Triple
T7595363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doom Patrol |
E179843
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Abigail Shapiro
Abigail Shapiro is an American actress best known for her role in the television series "Doom Patrol."
|
E674813
|
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: Abigail Shapiro | Statement: [Doom Patrol, stars, Abigail Shapiro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Abigail Shapiro Context triple: [Doom Patrol, stars, Abigail Shapiro]
-
A.
Abby Buchman
Abby Buchman is a central character in the drama film "Rachel Getting Married," around whom much of the story’s emotional tension and family dynamics revolve.
-
B.
Avriel Shull
Avriel Shull was a mid-20th-century American designer and builder known for her distinctive modernist residential architecture in Indiana.
-
C.
Abbie Steinhauser
Abbie Steinhauser is an architect known for her work on the design of the Van Abbemuseum.
-
D.
Sydney Shapiro
Sydney Shapiro is an American actress and former model best known as the wife of Uber CEO Dara Khosrowshahi.
-
E.
Abigail Falbury
Abigail Falbury is a fictional character portrayed by actress Gloria DeHaven, likely appearing in a mid-20th-century film or television production.
- 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: Abigail Shapiro Triple: [Doom Patrol, stars, Abigail Shapiro]
Generated description
Abigail Shapiro is an American actress best known for her role in the television series "Doom Patrol."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Abigail Shapiro Target entity description: Abigail Shapiro is an American actress best known for her role in the television series "Doom Patrol."
-
A.
Abby Buchman
Abby Buchman is a central character in the drama film "Rachel Getting Married," around whom much of the story’s emotional tension and family dynamics revolve.
-
B.
Avriel Shull
Avriel Shull was a mid-20th-century American designer and builder known for her distinctive modernist residential architecture in Indiana.
-
C.
Abbie Steinhauser
Abbie Steinhauser is an architect known for her work on the design of the Van Abbemuseum.
-
D.
Sydney Shapiro
Sydney Shapiro is an American actress and former model best known as the wife of Uber CEO Dara Khosrowshahi.
-
E.
Abigail Falbury
Abigail Falbury is a fictional character portrayed by actress Gloria DeHaven, likely appearing in a mid-20th-century film or television production.
- 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_69c69f3487ec8190bf7acdf2dd91e6d6 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9bbcd8081909a229d7faa2ffdc8 |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8619d6f2081908c8b589d4106691f |
completed | March 28, 2026, 11:17 p.m. |
| NEDg | Description generation | batch_69c86211e4f88190b38bce6441e33b53 |
completed | March 28, 2026, 11:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c862bb95e881909a60608a5279238d |
completed | March 28, 2026, 11:22 p.m. |
Created at: March 27, 2026, 3:53 p.m.