Triple
T6443219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sandino |
E138275
|
entity |
| Predicate | countryCode |
P208
|
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: [Sandino, countryCode, CU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CU Context triple: [Sandino, countryCode, CU]
-
A.
CU
CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
-
B.
CU
CU is the common abbreviation for the Christian Union, a Christian student organization found at many universities.
-
C.
CU
chosen
CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
-
D.
UCE
UCE is a major public university in Quito, Ecuador, recognized as one of the country’s oldest and most important higher education institutions.
-
E.
UC
UC is the commonly used abbreviation for Universal Credit, the United Kingdom’s main welfare benefit for people on a low income or out of work.
- 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_69c008aa61ac8190bc96715ed79fe2d8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0698b030481908b7bb4e16a8b3339 |
completed | March 22, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64bc718dc8190b186d09a17562d26 |
completed | March 27, 2026, 9:20 a.m. |
Created at: March 22, 2026, 4:46 p.m.