Triple
T5467183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwen Moore |
E122739
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gwen |
E355151
|
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: Gwen | Statement: [Gwen Moore, givenName, Gwen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gwen Context triple: [Gwen Moore, givenName, Gwen]
-
A.
Gwen
Gwen is the Allied reporting name for the Mitsubishi Ki-21, a Japanese twin-engine bomber used extensively during World War II.
-
B.
Gwen Cooper
Gwen Cooper is a compassionate yet tough Welsh police officer who becomes a key member of the secret alien-hunting team in the British sci-fi series Torchwood.
-
C.
Gwendolyn
chosen
Gwendolyn is a feminine given name most famously borne by the Pulitzer Prize–winning American poet Gwendolyn Brooks.
-
D.
Gwendoline
Gwendoline is a feminine given name most prominently associated with British actress Gwendoline Christie.
-
E.
Gemma Jones
Gemma Jones is an English actress known for her work in film, television, and theatre, including prominent roles in period dramas and popular franchises like the Bridget Jones series and the Harry Potter films.
- 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_69bd4643f16081908d7f29e08096115a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9218621c819093267a012bd49a35 |
completed | March 20, 2026, 6:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf488da1d8819091e1cad0500b1747 |
completed | March 22, 2026, 1:40 a.m. |
Created at: March 20, 2026, 2:08 p.m.