Triple
T19441855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chester Daniel Massey |
E486368
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Massey |
—
|
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: Massey | Statement: [Chester Daniel Massey, familyName, Massey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Massey Context triple: [Chester Daniel Massey, familyName, Massey]
-
A.
Massey
chosen
Massey is a surname most notably associated with Vincent Massey, the first Canadian-born Governor General of Canada.
-
B.
McCauley
McCauley is the maiden surname of Rosa Parks, the prominent American civil rights activist known for her pivotal role in the Montgomery bus boycott.
-
C.
Mathieson
Mathieson is a surname of Scottish origin, typically regarded as a variant of Matheson and derived from a patronymic meaning “son of Matthew.”
-
D.
Merrill
Merrill is the wealth management and brokerage division of Bank of America, offering investment advice, financial planning, and related services to individual and institutional clients.
-
E.
Merrill
Merrill is a given name used by various notable individuals across fields such as literature, entertainment, and business.
- 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63365343081908683c29f6c4c194b |
completed | April 20, 2026, 2:08 p.m. |
Created at: April 10, 2026, 1:38 p.m.