Triple
T19040716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannah Pingree |
E465992
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Hannah Pingree |
—
|
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: Hannah Pingree | Statement: [Hannah Pingree, name, Hannah Pingree]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hannah Pingree Context triple: [Hannah Pingree, name, Hannah Pingree]
-
A.
Hannah Pingree
chosen
Hannah Pingree is an American politician from Maine who has served as Speaker of the Maine House of Representatives and is the daughter of U.S. Representative Chellie Pingree.
-
B.
Rochelle Pingree
Rochelle Pingree is an individual whose legal name is formally registered as "Rochelle Pingree."
-
C.
Hazel Pingree
Hazel Pingree is a historic American shoe brand known for its high-quality, durable footwear.
-
D.
Jane Frazee
Jane Frazee was an American actress and singer best known for her roles in 1940s musical comedies and wartime films.
-
E.
Maureen Beattie
Maureen Beattie is a Scottish actress known for her extensive work in television, theatre, and film, including roles in British dramas and comedies.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d80054c88190a9d3a49aed504235 |
completed | April 20, 2026, 7:38 a.m. |
Created at: April 10, 2026, 12:02 p.m.