Triple
T1107269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Birán |
E25513
|
entity |
| Predicate | hasCountryCode |
P189
|
FINISHED |
| Object | CU |
E63203
|
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: CU | Statement: [Birán, hasCountryCode, CU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CU Context triple: [Birán, hasCountryCode, CU]
-
A.
CU
chosen
CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
-
B.
UC
UC is a public university in Canberra, Australia, known for its career-focused programs and strong industry partnerships.
-
C.
UC
UC is the final generation of the Holden Torana, a compact Australian car produced in the late 1970s.
-
D.
UC
UC is a leading Chilean university, widely recognized for its academic excellence and strong influence in education, research, and public policy in Latin America.
-
E.
UA
UA is the two-letter IATA airline designator used worldwide to identify United Airlines on tickets, schedules, and flight information.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9e47e4881908928900df72781f0 |
completed | March 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac53935d108190955343cd1d3716b0 |
completed | March 7, 2026, 4:34 p.m. |
Created at: March 1, 2026, 7:43 p.m.