Triple
T12909213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedman Memorial Airport |
E308807
|
entity |
| Predicate | FAAcode |
P420
|
FINISHED |
| Object | SUN |
E1009163
|
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: SUN | Statement: [Friedman Memorial Airport, FAAcode, SUN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SUN Context triple: [Friedman Memorial Airport, FAAcode, SUN]
-
A.
SUN
SUN is the National Rail station code for Sunderland railway station in Tyne and Wear, England.
-
B.
SUN
SUN is the Australian Securities Exchange (ASX) ticker symbol for Suncorp Group, a major Australian finance, insurance, and banking corporation.
-
C.
SUN
chosen
SUN is the IATA airport code for Friedman Memorial Airport, which serves the Sun Valley and Hailey area in Idaho, United States.
-
D.
Sun
The Sun is the massive, luminous star at the center of our solar system that provides the light and heat necessary for life on Earth.
-
E.
Sun
Sun I-hsien is a Taiwanese politician who has served in various governmental roles, including as a legislator.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9719e584c81909be1ac1366effca0 |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af5c133c81908b52fc18262c819d |
completed | May 3, 2026, 2:13 a.m. |
Created at: April 9, 2026, 5:41 p.m.