Triple
T2388871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Maine |
E48893
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Lisbon, Maine |
E187146
|
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: Lisbon, Maine | Statement: [Southern Maine, contains, Lisbon, Maine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisbon, Maine Context triple: [Southern Maine, contains, Lisbon, Maine]
-
A.
Lisbon, Maine
chosen
Lisbon, Maine is a small town in Androscoggin County known for its historic mill heritage and annual Moxie Festival celebrating the iconic soft drink.
-
B.
Naples, Maine
Naples, Maine is a small resort town in the Lakes Region of western Maine known for its waterfront recreation on Long Lake and Brandy Pond.
-
C.
Rome, Maine
Rome, Maine is a small rural town in central Maine known for its lakes, forests, and quiet residential character within Kennebec County.
-
D.
Belgrade, Maine
Belgrade, Maine is a small town in central Maine known for its scenic chain of lakes and rural New England character.
-
E.
Plymouth, Maine
Plymouth, Maine is a small rural town in Penobscot County known for its quiet residential character and forested, lake-dotted landscape.
- 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_69a88aa5f63081908d07fd302029fcbd |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc7dca9248190b634ae9e02f6899a |
completed | March 7, 2026, 6:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70057abc48190a5855ce1c6029d81 |
completed | March 27, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:57 p.m.