Triple
T21148710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Keyes |
E521127
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Keyes |
—
|
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: Keyes | Statement: [Robert Keyes, familyName, Keyes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keyes Context triple: [Robert Keyes, familyName, Keyes]
-
A.
Keyes
Keyes is a small unincorporated community located in Stanislaus County in California’s Central Valley.
-
B.
Keyes
chosen
Keyes is the surname of American author Daniel Keyes, best known for writing the science fiction classic "Flowers for Algernon."
-
C.
Klyuchi
Klyuchi is a rural settlement in Russia’s Kamchatka Peninsula known primarily as the closest community to the active Klyuchevskoy volcano.
-
D.
Leavey
Leavey is a surname most notably associated with Megan Leavey, a former U.S. Marine corporal known for her service as a military dog handler in Iraq.
-
E.
Keeley
Keeley is a feminine given name of English origin, often used both as a first name and surname.
- 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_69e0b50c6a848190a4e525a77a319b8a |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e723ffb578819080a000987459a8a5 |
completed | April 21, 2026, 7:15 a.m. |
Created at: April 16, 2026, 2:58 p.m.