Triple
T7166884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwendoline Mary Lacey |
E167091
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Gwendoline |
E251216
|
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: Gwendoline | Statement: [Gwendoline Mary Lacey, hasGivenName, Gwendoline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gwendoline Context triple: [Gwendoline Mary Lacey, hasGivenName, Gwendoline]
-
A.
Gwendoline
chosen
Gwendoline is a feminine given name most prominently associated with British actress Gwendoline Christie.
-
B.
Gwendolyn
Gwendolyn is a feminine given name most famously borne by the Pulitzer Prize–winning American poet Gwendolyn Brooks.
-
C.
Winifred
Winifred is the given name of Winnie Madikizela-Mandela, the prominent South African anti-apartheid activist and politician.
-
D.
Glynis
Glynis is a feminine given name most notably associated with the British actress and singer Glynis Johns.
-
E.
Gwen
Gwen is the Allied reporting name for the Mitsubishi Ki-21, a Japanese twin-engine bomber used extensively during World War II.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e85a07388190a07054ef12870fa1 |
completed | March 27, 2026, 8:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7adced6b48190bcae9af88f640584 |
completed | March 28, 2026, 10:30 a.m. |
Created at: March 27, 2026, 2:48 p.m.