Triple
T15359072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GØGGS |
E367239
|
entity |
| Predicate | hasCoreMember |
P20032
|
FINISHED |
| Object | Chris Shaw |
E1154218
|
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: Chris Shaw | Statement: [GØGGS, hasCoreMember, Chris Shaw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Shaw Context triple: [GØGGS, hasCoreMember, Chris Shaw]
-
A.
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.
-
B.
Chris Shaw
chosen
Chris Shaw is a musician best known as a member of the garage rock band GØGGS.
-
C.
Ian Shaw
Ian Shaw is a British actor and writer, known for his stage and screen work and for being the son of acclaimed actor Robert Shaw.
-
D.
Tom Shaw
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.
-
E.
Mark Shaw
Mark Shaw is best known as the husband of American singer and actress Pat Suzuki.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2d4934819097fc63603964217c |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a6991148190b522684b35c07b1a |
completed | May 9, 2026, 11:28 a.m. |
Created at: April 10, 2026, 3:18 a.m.