Triple
T22200323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomery County Airpark |
E548662
|
entity |
| Predicate | hasIATACode |
P2569
|
FINISHED |
| Object | GAI |
—
|
NE NERFINISHED |
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: GAI | Statement: [Montgomery County Airpark, hasIATACode, GAI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GAI Context triple: [Montgomery County Airpark, hasIATACode, GAI]
-
A.
GAI
chosen
GAI is the IATA airport code for Montgomery County Airpark, a public general aviation airport serving the Gaithersburg, Maryland area in the United States.
-
B.
Gayk
Gayk is an Armenian given name most notably borne by Soviet military commander Gayk Bzhishkyan.
-
C.
xAI
xAI is an artificial intelligence company focused on developing advanced AI systems, founded and led by entrepreneur Elon Musk.
-
D.
GAISh
GAISh is the commonly used abbreviation for the Sternberg Astronomical Institute, a major astronomical research and education center in Moscow, Russia.
-
E.
GIO
GIO is an Australian insurance company best known for providing a wide range of general insurance products, including car, home, and business insurance.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e3ecc7c8190b5f94cd8f42e9d37 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12aea51d48190a570cd36c106ab78 |
completed | April 28, 2026, 9:47 p.m. |
Created at: April 16, 2026, 8:36 p.m.