Triple
T16061503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China United Airlines |
E389623
|
entity |
| Predicate | ICAOCode |
P419
|
FINISHED |
| Object | CUA |
E389624
|
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: CUA | Statement: [China United Airlines, ICAOCode, CUA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CUA Context triple: [China United Airlines, ICAOCode, CUA]
-
A.
CUA
chosen
CUA is the ICAO airline designator for China United Airlines, a Chinese domestic carrier based in Beijing.
-
B.
CUA
CUA is a joint MIT–Harvard research center focused on the study of ultracold atomic physics and quantum phenomena.
-
C.
CAU
CAU is the MIT Center for Advanced Urbanism, a research hub focused on innovative design and policy solutions for contemporary urban challenges.
-
D.
UCA
UCA is a Spanish public university based in Cádiz, known for its programs in marine sciences, engineering, and humanities.
-
E.
UCA
UCA is the Unicode Collation Algorithm, a Unicode standard that defines a language-independent method for ordering and comparing Unicode text.
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183795100819097be92e6d07dc5b1 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbe88a608190bc0a0cbfdb71e81d |
completed | May 10, 2026, 1:14 a.m. |
Created at: April 10, 2026, 4:57 a.m.