Triple
T16314842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian Union |
E396147
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | CU |
E396147
|
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: [Christian Union, abbreviation, CU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CU Context triple: [Christian Union, abbreviation, CU]
-
A.
CU
CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
-
B.
CU
CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
-
C.
CU
CU is the commonly used abbreviation for Ciudad Universitaria, a major university campus area in Spanish-speaking regions.
-
D.
CU
CU is the commonly used abbreviation for the multi-campus University of Colorado public university system in the United States.
-
E.
CU
chosen
CU is the common abbreviation for the Christian Union, a Christian student organization found at many universities.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e288de57cc81908cec93309347c385 |
completed | April 17, 2026, 7:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001fa88e8c8190b0423b60389896dc |
completed | May 10, 2026, 6:03 a.m. |
Created at: April 10, 2026, 5:06 a.m.