Triple
T15761379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madeline Zima |
E382104
|
entity |
| Predicate | hasSibling |
P363
|
FINISHED |
| Object | Vanessa Zima |
E384683
|
NE FINISHED |
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: Vanessa Zima | Statement: [Madeline Zima, hasSibling, Vanessa Zima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vanessa Zima Context triple: [Madeline Zima, hasSibling, Vanessa Zima]
-
A.
Vanessa Zima
chosen
Vanessa Zima is an American actress known for her roles in films such as "Ulee's Gold" and "The Brain."
-
B.
Vanessa Brown
Vanessa Brown was an Austrian-born American actress known for her work in mid-20th-century Hollywood films, radio, and stage productions.
-
C.
Vanessa Roth
Vanessa Roth is an Academy Award-winning American documentary filmmaker known for her socially conscious films and work in education and social justice.
-
D.
Vanessa Ferlito
Vanessa Ferlito is an American actress known for her roles in films like "Death Proof" and TV series such as "CSI: NY" and "NCIS: New Orleans."
-
E.
Vanessa Loring
Vanessa Loring is a key supporting character in the film "Juno," portrayed as a woman longing to adopt a child and struggling with the complexities of marriage and motherhood.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b52c548190a0ffa4493a4eb15c |
completed | April 16, 2026, 3 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff998397688190a77b6a7c5b542f7e |
completed | May 9, 2026, 8:30 p.m. |
Created at: April 10, 2026, 4:47 a.m.