Triple
T4515511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamar Teresa Day Hennessy |
E102143
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Vermont |
E9978
|
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: Vermont | Statement: [Tamar Teresa Day Hennessy, residence, Vermont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vermont Context triple: [Tamar Teresa Day Hennessy, residence, Vermont]
-
A.
Vermont
chosen
Vermont is a small, rural New England state in the northeastern United States, known for its Green Mountains, maple syrup production, and picturesque towns.
-
B.
Vermont
Vermont is a small rural town located in Dane County, Wisconsin, known for its scenic landscapes and agricultural character.
-
C.
New Hampshire
New Hampshire is a small New England state in the northeastern United States known for its mountainous landscapes, early presidential primary, and “Live Free or Die” motto.
-
D.
New Hampshire and Vermont
New Hampshire and Vermont are two neighboring New England states in the northeastern United States, known for their rural landscapes, small towns, and shared border along the Connecticut River.
-
E.
Maine
Maine is a northeastern U.S. state known for its rugged coastline, maritime history, and vast forested interior.
- 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_69bd43d6251c81909deecce3e6e9d69c |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5725745c81908bb462ba9537ae14 |
completed | March 20, 2026, 2:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bda42bd41c8190a9a25ccea6947089 |
completed | March 20, 2026, 7:46 p.m. |
Created at: March 20, 2026, 1:02 p.m.