Triple
T1500732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vivian Lake Brady |
E29789
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Vivian |
E95915
|
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: Vivian | Statement: [Vivian Lake Brady, givenName, Vivian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vivian Context triple: [Vivian Lake Brady, givenName, Vivian]
-
A.
Vivian
chosen
Vivian "Buster" Burey Marshall was a civil rights activist and the first wife of U.S. Supreme Court Justice Thurgood Marshall.
-
B.
Vanessa
Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
-
C.
Violet Barnes
Violet Barnes is a central character in the romantic comedy film "The Five-Year Engagement," portrayed as an ambitious academic whose prolonged engagement tests her relationship and personal aspirations.
-
D.
Evelyn
Evelyn is a given name shared by G. Evelyn Hutchinson, a prominent 20th-century British-born American ecologist often called the "father of modern ecology."
-
E.
Junior Viviane
Junior Viviane is a fictional character appearing in Toni Morrison’s novel "Love."
- 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_69a498dba1d8819093b46a3a8d2485f1 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6f2d7f881909188a3e5614335cd |
completed | March 1, 2026, 11:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeabea0d88190b0bd83aece8c7b55 |
completed | March 8, 2026, 9:31 p.m. |
Created at: March 1, 2026, 8:12 p.m.