Triple
T18304013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penobscot River watershed |
E438434
|
entity |
| Predicate | includesCity |
P3207
|
FINISHED |
| Object | Orono |
—
|
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: Orono | Statement: [Penobscot River watershed, includesCity, Orono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orono Context triple: [Penobscot River watershed, includesCity, Orono]
-
A.
Orono
Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
-
B.
Orono
Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
-
C.
Orono, Maine
chosen
Orono, Maine is a small town in Penobscot County best known as the home of the University of Maine’s flagship campus.
-
D.
Gardiner
Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
-
E.
Gardiner
Gardiner is a residential suburb in Melbourne, Victoria, known for its access to public transport and proximity to the city’s inner east.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5018256508190b024980e3a4a3ae9 |
completed | April 19, 2026, 4:23 p.m. |
Created at: April 10, 2026, 10:35 a.m.