Triple
T5807231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomery County Airpark |
E128775
|
entity |
| Predicate | FAA LID |
P420
|
FINISHED |
| Object | GAI |
E548662
|
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: GAI | Statement: [Montgomery County Airpark, FAA LID, GAI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GAI Context triple: [Montgomery County Airpark, FAA LID, 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.
xAI
xAI is an artificial intelligence company focused on developing advanced AI systems, founded and led by entrepreneur Elon Musk.
-
C.
GIO
GIO is an Australian insurance company best known for providing a wide range of general insurance products, including car, home, and business insurance.
-
D.
Grok
Grok is an AI chatbot developed by xAI, designed to provide conversational access to real-time information and reasoning capabilities.
-
E.
GIG
GIG is the IATA airport code for Rio de Janeiro–Galeão International Airport, the main international gateway serving Rio de Janeiro, Brazil.
- 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_69c00846a0d881909e46841f8e156b64 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02b17417081908779741b9bfbb720 |
completed | March 22, 2026, 5:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a17f77fc8190b2ad6f6c45d96e43 |
completed | March 23, 2026, 2:12 a.m. |
Created at: March 22, 2026, 3:52 p.m.