Triple
T1479232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollywood Burbank Airport |
E30913
|
entity |
| Predicate | FAAcode |
P420
|
FINISHED |
| Object | BUR |
E169015
|
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: BUR | Statement: [Hollywood Burbank Airport, FAAcode, BUR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BUR Context triple: [Hollywood Burbank Airport, FAAcode, BUR]
-
A.
BUR
chosen
BUR is the three-letter IATA airport code for Hollywood Burbank Airport, a commercial airport serving the Los Angeles area in Southern California.
-
B.
BR
BR is the upper house of Austria’s parliament, representing the federal states in the legislative process.
-
C.
BR
BR is the two-letter ISO 3166-1 alpha-2 country code that uniquely identifies Brazil in international standards and systems.
-
D.
BER
BER is Berlin Brandenburg Airport, the main international airport serving Germany’s capital region.
-
E.
BU
BU is a major private research university in Boston, Massachusetts, known for its diverse academic programs and global student body.
- 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_69a498fe55a88190ab7f9e40ace88e49 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c6739d2481909ea8d8e075f62cf3 |
completed | March 1, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad1ca21f288190b5f6f9a5895cdcf0 |
completed | March 8, 2026, 6:52 a.m. |
Created at: March 1, 2026, 8:11 p.m.