Triple
T19118355
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Crocker |
E467969
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Crocker |
—
|
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: Crocker | Statement: [George Crocker, familyName, Crocker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Crocker Context triple: [George Crocker, familyName, Crocker]
-
A.
Crocker
chosen
Crocker is a surname of English origin borne by various notable individuals across fields such as technology, sports, and the arts.
-
B.
Gundy
Gundy is a surname most prominently associated with American college football coach Mike Gundy.
-
C.
Dymond
Dymond is a former township in Ontario, Canada, that was amalgamated into the city of Temiskaming Shores.
-
D.
Kemper
Kemper is a surname most prominently associated with the American banking and philanthropic family involved in finance, arts, and education.
-
E.
Longren
Longren is a central character in Alexander Grin’s romantic novel "Scarlet Sails," portrayed as a withdrawn former sailor and devoted father whose past and grief shape the story’s emotional landscape.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e3c756a88190942930e6ae7242a7 |
completed | April 20, 2026, 8:28 a.m. |
Created at: April 10, 2026, 12:05 p.m.