Triple
T8573203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gertrude Ederle |
E202978
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Trudy |
E202978
|
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: Trudy | Statement: [Gertrude Ederle, nickname, Trudy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trudy Context triple: [Gertrude Ederle, nickname, Trudy]
-
A.
Trudy
chosen
Trudy is the nickname of Gertrude Ederle, the American competitive swimmer who became the first woman to swim across the English Channel.
-
B.
Trudy
Trudy is a feminine given name, often used as a diminutive of names like Ermintrude or Gertrude.
-
C.
Trisha
Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
-
D.
Leatrice Joy
Leatrice Joy was a prominent American silent film actress of the 1920s known for her expressive performances and distinctive bobbed hairstyle.
-
E.
Judy
"Judy" is a 2019 biographical drama film in which Renée Zellweger portrays legendary entertainer Judy Garland during her final years, a role that earned her widespread acclaim and major acting awards.
- 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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea458c1081908e79bee2cbf97207 |
completed | March 31, 2026, 3:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce898cf8648190b52758b6ecf2959b |
completed | April 2, 2026, 3:21 p.m. |
Created at: March 30, 2026, 6:21 p.m.