Triple
T17723939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bethany Hamilton |
E442410
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object | Beth |
—
|
NE NERFINISHED |
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: Beth | Statement: [Bethany Hamilton, hasNickname, Beth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beth Context triple: [Bethany Hamilton, hasNickname, Beth]
-
A.
Beth
chosen
Beth is a common shortened form of the given name Elizabeth, often used as a standalone feminine first name.
-
B.
Amy
Amy is a critically acclaimed 2015 documentary film about the life and career of British singer-songwriter Amy Winehouse.
-
C.
Amy
Amy is a common feminine given name used in many English-speaking countries.
-
D.
Emily
Emily Warren Roebling was a pioneering 19th-century American engineer best known for her crucial role in overseeing the completion of the Brooklyn Bridge.
-
E.
Emily
Emily is a given name commonly used in English-speaking countries, often associated with literary, historical, and contemporary cultural figures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9ec79688190b86bdcef85a7b3aa |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4748900608190bf5ba04415edaffc |
completed | April 19, 2026, 6:22 a.m. |
Created at: April 10, 2026, 10:07 a.m.