Triple
T22122870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne of Green Gables (1934 film) |
E546715
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Tom Brown |
—
|
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: Tom Brown | Statement: [Anne of Green Gables (1934 film), stars, Tom Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Brown Context triple: [Anne of Green Gables (1934 film), stars, Tom Brown]
-
A.
Tom Brown
Tom Brown is a fictional character appearing in the 1930 American film "Morocco," which stars Marlene Dietrich and Gary Cooper.
-
B.
Tom Brown
chosen
Tom Brown was an American child and later character actor known for his roles in early 20th-century films and radio, including appearances in classic comedies and dramas.
-
C.
Tom Brown
Tom Brown is a technology entrepreneur best known as a co-founder of the AI safety and research company Anthropic.
-
D.
Ben Brown
Ben Brown is a British journalist and news presenter best known for his long-standing role as a BBC News anchor.
-
E.
Hobart Brown
Hobart Brown was an American artist and sculptor best known as the eccentric founder of the human-powered art race tradition that became the Kinetic Grand Championship.
- 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_69e11e38b3848190ac3a4fa97d56e65a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1297f3fb48190b6aaca18b40c37ab |
completed | April 28, 2026, 9:41 p.m. |
Created at: April 16, 2026, 8:31 p.m.