Triple
T20329984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Cohen |
E492449
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Cohen |
E109009
|
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: Cohen | Statement: [Betty Cohen, familyName, Cohen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cohen Context triple: [Betty Cohen, familyName, Cohen]
-
A.
Cohen
chosen
Cohen is a common Jewish surname of Hebrew origin historically associated with priestly lineage.
-
B.
Cohn
Cohn is a surname most prominently associated with Harry Cohn, the influential co-founder and longtime head of Columbia Pictures.
-
C.
Cohon
Cohon is the family surname of American actor, author, and narrator Peter Coyote, born Peter Cohon.
-
D.
Cohan
Cohan is a surname most famously associated with American entertainer, composer, and playwright George M. Cohan.
-
E.
Cohen-Kagan
Cohen-Kagan is a Hebrew-language surname most notably borne by Israeli politician and women's rights activist Rachel Cohen-Kagan.
- 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_69e0b4a1a09881908d97270d6971a25a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e677e6f5d08190bedf376bbb999ebc |
completed | April 20, 2026, 7 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a086951af848190ae8085b7917ea551 |
completed | May 16, 2026, 12:55 p.m. |
Created at: April 16, 2026, 11:22 a.m.