Triple
T1216623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Newfoundland and Labrador |
E26119
|
entity |
| Predicate | ISOCode |
P208
|
FINISHED |
| Object |
CA-NL
CA-NL is the ISO 3166-2 subdivision code representing the Canadian province of Newfoundland and Labrador.
|
E139839
|
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: CA-NL | Statement: [Newfoundland and Labrador, ISOCode, CA-NL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CA-NL Context triple: [Newfoundland and Labrador, ISOCode, CA-NL]
-
A.
New Brunswick, Canada
New Brunswick, Canada is a maritime province on the Atlantic coast known for its bilingual English-French culture, extensive forests, and the Bay of Fundy’s dramatic tides.
-
B.
Kanata—Carleton
Kanata—Carleton is a Canadian federal electoral district in Ontario that includes the western suburbs and rural areas of Ottawa.
-
C.
Ontario
Ontario is Canada’s most populous province, home to the nation’s capital Ottawa and its largest city Toronto, and a major economic and cultural hub.
-
D.
Ontario
Ontario is a city in southwestern San Bernardino County, California, known as a major logistics and transportation hub anchored by Ontario International Airport and extensive freeway and rail connections.
-
E.
CANADIAN
CANADIAN was the radio callsign used by Canadian Airlines for its commercial flight operations.
- 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: CA-NL Triple: [Newfoundland and Labrador, ISOCode, CA-NL]
Generated description
CA-NL is the ISO 3166-2 subdivision code representing the Canadian province of Newfoundland and Labrador.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CA-NL Target entity description: CA-NL is the ISO 3166-2 subdivision code representing the Canadian province of Newfoundland and Labrador.
-
A.
New Brunswick, Canada
New Brunswick, Canada is a maritime province on the Atlantic coast known for its bilingual English-French culture, extensive forests, and the Bay of Fundy’s dramatic tides.
-
B.
Kanata—Carleton
Kanata—Carleton is a Canadian federal electoral district in Ontario that includes the western suburbs and rural areas of Ottawa.
-
C.
Ontario
Ontario is Canada’s most populous province, home to the nation’s capital Ottawa and its largest city Toronto, and a major economic and cultural hub.
-
D.
Ontario
Ontario is a city in southwestern San Bernardino County, California, known as a major logistics and transportation hub anchored by Ontario International Airport and extensive freeway and rail connections.
-
E.
CANADIAN
CANADIAN was the radio callsign used by Canadian Airlines for its commercial flight operations.
- 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_69a4948331fc8190b531ac9bec71c491 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be06d6308190a44c505e6b5e8d42 |
completed | March 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac831fb6bc8190907f36e52489ec5c |
completed | March 7, 2026, 7:57 p.m. |
| NEDg | Description generation | batch_69ac838fc3d08190aa43d7f2767fe7d2 |
completed | March 7, 2026, 7:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac8425ab408190a25c0f5db40ae77f |
completed | March 7, 2026, 8:01 p.m. |
Created at: March 1, 2026, 7:46 p.m.