Triple
T4287667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexico, Maine |
E97307
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Peru, Maine |
E168754
|
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: Peru, Maine | Statement: [Mexico, Maine, borderedBy, Peru, Maine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peru, Maine Context triple: [Mexico, Maine, borderedBy, Peru, Maine]
-
A.
Peru, Maine
chosen
Peru, Maine is a small rural town in Oxford County known for its scenic western Maine landscape and close-knit community.
-
B.
Freedom, Maine
Freedom, Maine is a small rural town in central Maine known for its scenic countryside and tight-knit community.
-
C.
Lebanon, Maine
Lebanon, Maine is a small rural town in southwestern Maine known for its forests, lakes, and proximity to the New Hampshire border.
-
D.
Strong, Maine
Strong, Maine is a small rural town in western Maine known historically for its lumber and toothpick manufacturing industries.
-
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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3505ef9fc81909c73e64bf77052ce |
completed | March 12, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c72bec388190b03048507e2e6858 |
completed | March 14, 2026, 8:38 p.m. |
Created at: March 12, 2026, 11:08 p.m.