Triple
T20019320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Gilbert |
E494810
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Gilbert |
—
|
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: Gilbert | Statement: [Michael Gilbert, familyName, Gilbert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gilbert Context triple: [Michael Gilbert, familyName, Gilbert]
-
A.
Gilbert
Gilbert is a rapidly growing suburban town in the southeastern Phoenix metropolitan area known for its family-friendly communities and high quality of life.
-
B.
Gilbert
Gilbert is a renowned sports equipment manufacturer best known for producing high-quality rugby balls used in major international competitions.
-
C.
Gilbert
Gilbert is a large impact crater on the Moon’s near side, located southeast of the prominent crater Mare Crisium.
-
D.
Gilbert
chosen
Gilbert is a masculine given name of Norman-French origin that has been borne by various notable figures, including philosophers, writers, and entertainers.
-
E.
Niles
Niles is the witty, sarcastic butler from the sitcom "The Nanny," known for his sharp one-liners and ongoing rivalry with C.C. Babcock.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6623e40748190b1abb0ead9acab4e |
completed | April 20, 2026, 5:28 p.m. |
Created at: April 11, 2026, 3:34 p.m.