Triple
T1234479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wendy Hall |
E26515
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Wendy |
E46511
|
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: Wendy | Statement: [Wendy Hall, givenName, Wendy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wendy Context triple: [Wendy Hall, givenName, Wendy]
-
A.
Wendy
chosen
Wendy is a character portrayed by actress and model Jamie King, known from her work in film and television.
-
B.
Betty
Betty is the childhood nickname of Elizabeth Parris, the young girl whose strange afflictions helped spark the Salem witch trials in 1692.
-
C.
Sally
Sally is the given name of Sally K. Ride, the American physicist and astronaut who became the first American woman in space.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Carla
Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
- 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_69a4948571c88190a9191e451e6035fd |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be5e421081908f2432528019db25 |
completed | March 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8a16badc8190b5b603db0ca738cb |
completed | March 7, 2026, 8:27 p.m. |
Created at: March 1, 2026, 7:47 p.m.