Triple
T16474462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fanny Shaw |
E400150
|
entity |
| Predicate | hasSibling |
P363
|
FINISHED |
| Object | Tom Shaw |
E400151
|
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: Tom Shaw | Statement: [Fanny Shaw, hasSibling, Tom Shaw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Shaw Context triple: [Fanny Shaw, hasSibling, Tom Shaw]
-
A.
Tom Shaw
chosen
Tom Shaw is a central character in Louisa May Alcott’s novel "An Old-Fashioned Girl," portrayed as a kind but initially worldly young man whose growth and changing values mirror the story’s moral themes.
-
B.
Chris Shaw
Chris Shaw is a musician best known as a member of the garage rock band GØGGS.
-
C.
Chris Shaw
Chris Shaw is an American professional baseball player and power-hitting outfielder/first baseman who played college baseball at Boston College before reaching Major League Baseball.
-
D.
Mark Shaw
Mark Shaw is best known as the husband of American singer and actress Pat Suzuki.
-
E.
Dennis Shaw
Dennis Shaw is a former American football quarterback who starred at San Diego State before playing in the NFL, most notably for the Buffalo Bills.
- 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32dd32e048190a9eadd32d6b9374c |
completed | April 18, 2026, 7:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006ed25bcc819090ccca4705e4e24f |
completed | May 10, 2026, 11:41 a.m. |
Created at: April 10, 2026, 5:13 a.m.