Triple
T10133723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bangor, County Down |
E226801
|
entity |
| Predicate | postTown |
P2711
|
FINISHED |
| Object |
BANGOR
BANGOR is a coastal town in County Down, Northern Ireland, known as a seaside resort and commuter town for Belfast.
|
E843200
|
NE FINISHED |
How this triple was built (4 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: BANGOR | Statement: [Bangor, County Down, postTown, BANGOR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BANGOR Context triple: [Bangor, County Down, postTown, BANGOR]
-
A.
Bangor, Maine
Bangor, Maine is a small city in eastern Maine known as a regional commercial and cultural hub and famously associated with author Stephen King.
-
B.
Biddeford, Maine
Biddeford, Maine is a historic mill city in York County known for its revitalized downtown along the Saco River and its role as a regional economic and educational center.
-
C.
Portland, Maine
Portland, Maine is a coastal New England city known for its historic Old Port district, vibrant arts and food scenes, and working waterfront on Casco Bay.
-
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.
Oakland, Maine
Oakland, Maine is a small town in central Maine known for its lakeside setting, residential character, and proximity to the city of Waterville.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: BANGOR Triple: [Bangor, County Down, postTown, BANGOR]
Generated description
BANGOR is a coastal town in County Down, Northern Ireland, known as a seaside resort and commuter town for Belfast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BANGOR Target entity description: BANGOR is a coastal town in County Down, Northern Ireland, known as a seaside resort and commuter town for Belfast.
-
A.
Bangor, Maine
Bangor, Maine is a small city in eastern Maine known as a regional commercial and cultural hub and famously associated with author Stephen King.
-
B.
Biddeford, Maine
Biddeford, Maine is a historic mill city in York County known for its revitalized downtown along the Saco River and its role as a regional economic and educational center.
-
C.
Portland, Maine
Portland, Maine is a coastal New England city known for its historic Old Port district, vibrant arts and food scenes, and working waterfront on Casco Bay.
-
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.
Oakland, Maine
Oakland, Maine is a small town in central Maine known for its lakeside setting, residential character, and proximity to the city of Waterville.
- F. None of above. chosen
Provenance (5 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_69ca8433ec308190b8b25a6fe359c34c |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd33808048190b6023a6cd83fc179 |
completed | April 2, 2026, 2:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e5d98a148190ada7082adb98ddaa |
completed | April 5, 2026, 10:44 p.m. |
| NEDg | Description generation | batch_69d2e73e4d5081909f0068d3bed583d3 |
completed | April 5, 2026, 10:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2e7eea4d88190a2ec6d22a83934b3 |
completed | April 5, 2026, 10:53 p.m. |
Created at: March 30, 2026, 9:06 p.m.