Triple
T11061552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EMCC |
E261518
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Bangor |
E18442
|
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: Bangor | Statement: [EMCC, city, Bangor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bangor Context triple: [EMCC, city, Bangor]
-
A.
Bangor
Bangor is a coastal town in Northern Ireland known for its marina, seaside resort heritage, and role as a commuter hub for nearby Belfast.
-
B.
Bangor
Bangor is a historic cathedral city in northwest Wales, known for its university and scenic location near the Menai Strait.
-
C.
Bangor metropolitan area
The Bangor metropolitan area is a regional urban and economic hub in central-eastern Maine centered on the city of Bangor and its surrounding communities.
-
D.
BANGOR
BANGOR is a coastal town in County Down, Northern Ireland, known as a seaside resort and commuter town for Belfast.
-
E.
Bangor, Maine
chosen
Bangor, Maine is a small city in eastern Maine known as a regional commercial and cultural hub and famously associated with author Stephen King.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798ea834c819099401e69f995c59f |
completed | April 9, 2026, 12:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c887d2148190b19c91b6eb548494 |
completed | April 18, 2026, 6:08 p.m. |
Created at: April 8, 2026, 9:26 p.m.