Triple
T4264840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marianna, Florida |
E96798
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Mary Ann |
E96798
|
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: Mary Ann | Statement: [Marianna, Florida, namedAfter, Mary Ann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Ann Context triple: [Marianna, Florida, namedAfter, Mary Ann]
-
A.
Mary Ann
chosen
Mary Ann is the namesake of the city of Marianna in Florida.
-
B.
Betsy
Betsy is a common diminutive or nickname for the given name Elizabeth.
-
C.
Betsy
Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
-
D.
Mary Anne Grindall
Mary Anne Grindall was the wife of British civil servant, political reformer, and ornithologist Allan Octavian Hume.
-
E.
Ann Eliza
Ann Eliza is a historical figure known primarily as the namesake and given name of Ann Eliza Birney Russell.
- 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_69b34543f06c8190915ebb1a4574ffa9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34fcad0a881908e1cac0a6da5a321 |
completed | March 12, 2026, 11:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c71c9b488190abbca16d3ea70ae8 |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 12, 2026, 11:06 p.m.