Triple
T6904038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theophilus Eaton |
E159561
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | John Davenport |
E241743
|
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: John Davenport | Statement: [Theophilus Eaton, associatedWith, John Davenport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Davenport Context triple: [Theophilus Eaton, associatedWith, John Davenport]
-
A.
John Davenport
chosen
John Davenport was a prominent 17th-century English Puritan clergyman and co-founder of the New Haven Colony in New England.
-
B.
George Davenport
George Davenport was a 19th-century American fur trader and early settler whose influence on the region led to the city of Davenport, Iowa being named in his honor.
-
C.
Nathan Appleton
Nathan Appleton was a prominent 19th-century American merchant, industrialist, and politician who played a key role in the early textile industry in New England.
-
D.
Daniel Lothrop
Daniel Lothrop was a 19th-century American publisher best known for founding the D. Lothrop Company, which specialized in children's and religious literature.
-
E.
Gene Milford
Gene Milford was an American film editor known for his work on numerous classic Hollywood films across several decades.
- 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_69c6883822e0819091e321526f20ae0a |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d989c13081908a2e346cde9e3a50 |
completed | March 27, 2026, 7:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c75124cab88190a1510d77ed61c945 |
completed | March 28, 2026, 3:55 a.m. |
Created at: March 27, 2026, 2:25 p.m.