Triple
T8910545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vera Lynn |
E212170
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Vera |
E328915
|
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: Vera | Statement: [Vera Lynn, givenName, Vera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vera Context triple: [Vera Lynn, givenName, Vera]
-
A.
Vera
Vera is a memorable supporting character from the 1989 Eddie Murphy film "Harlem Nights," known for her tough, comedic persona.
-
B.
Vera
Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
-
C.
Vera
chosen
Vera is a feminine given name of Slavic origin, commonly used in Russian and other Eastern European cultures, meaning "faith."
-
D.
Veronika
Veronika is the troubled young protagonist of Paulo Coelho's novel "Veronika Decides to Die," whose suicide attempt leads her to a transformative stay in a mental institution.
-
E.
Veronika
Veronika is the tragic, resilient young woman at the heart of the Soviet World War II film "The Cranes Are Flying," whose life and love are shattered by the war.
- 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_69ca839255248190b43984294abd92ae |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc65227d008190b13ba162d0b3c9d1 |
completed | April 1, 2026, 12:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfba36f8cc8190ab57ddc99b7219d1 |
completed | April 3, 2026, 1:01 p.m. |
Created at: March 30, 2026, 6:55 p.m.