Triple
T17984839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stacy Barrett |
E430202
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | April Bowlby |
—
|
NE NERFINISHED |
How this triple was built (2 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: April Bowlby | Statement: [Stacy Barrett, portrayedBy, April Bowlby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: April Bowlby Context triple: [Stacy Barrett, portrayedBy, April Bowlby]
-
A.
April Bowlby
chosen
April Bowlby is an American actress best known for her television roles in series such as Drop Dead Diva, Two and a Half Men, and Doom Patrol.
-
B.
Dorothy Bishop
Dorothy Bishop is a British neuropsychologist and professor renowned for her research on developmental language disorders and advocacy for open science and research integrity.
-
C.
Ann Belsky
Ann Belsky was a costume designer and the late wife of Canadian actor Rick Moranis.
-
D.
John Bowlby
John Bowlby was a British psychiatrist and psychoanalyst best known for developing attachment theory, which transformed understanding of child development and the impact of early relationships on later mental health.
-
E.
Michael Tronick
Michael Tronick is an American film editor known for his work on numerous major Hollywood productions across several decades.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90364248190a37381adea932f42 |
completed | April 10, 2026, 8:46 a.m. |
| NER | Named-entity recognition | batch_69e4b29a27b081909a128a6b978eabf8 |
completed | April 19, 2026, 10:46 a.m. |
Created at: April 10, 2026, 10:23 a.m.