Triple
T17593268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Ann Seton |
E428498
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Seton |
—
|
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: Seton | Statement: [Elizabeth Ann Seton, familyName, Seton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seton Context triple: [Elizabeth Ann Seton, familyName, Seton]
-
A.
Seton
chosen
Seton is a given name most notably borne by American screenwriter and producer Seton I. Miller, known for his work in classic Hollywood cinema.
-
B.
Rose of Saint Mary
Rose of Saint Mary is another name for Saint Rose of Lima, the 17th-century Peruvian mystic venerated as the first canonized saint of the Americas.
-
C.
Loretto
Loretto is a small town in central Kentucky best known as the home of the Maker’s Mark bourbon distillery.
-
D.
Loretto
Loretto is a small city located in Hennepin County in the U.S. state of Minnesota.
-
E.
MacKillop
MacKillop is a rural electoral district in South Australia, known for its agricultural communities and expansive regional landscapes.
- 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e89acc81908e52138ad4f452c6 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.