Triple
T6687815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 17L/35R |
E152142
|
entity |
| Predicate | servesAirport |
P6864
|
FINISHED |
| Object | MCO |
E148977
|
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: MCO | Statement: [Runway 17L/35R, servesAirport, MCO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MCO Context triple: [Runway 17L/35R, servesAirport, MCO]
-
A.
MCO
MCO is the National Rail station code used to identify Oxford Road railway station in Manchester, England.
-
B.
MCO
MCO is the three-letter ISO 3166-1 alpha-3 country code assigned to the Principality of Monaco.
-
C.
MCO
chosen
MCO is the IATA airport code for Orlando International Airport, a major air travel hub serving the Orlando, Florida metropolitan area and its tourist attractions.
-
D.
MCO
MCO is the stock ticker symbol for Moody's Corporation, a leading global provider of credit ratings, research, and risk analysis.
-
E.
MCoE
MCoE is the U.S. Army’s primary training and doctrine center for maneuver forces, integrating infantry, armor, and related capabilities at Fort Moore, Georgia.
- 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_69c687f9977c819097e7f5ada4fe522e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b14e58708190a4ba8ff1c085f160 |
completed | March 27, 2026, 4:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723b8a5288190a2fb4d956f2dc385 |
completed | March 28, 2026, 12:41 a.m. |
Created at: March 27, 2026, 2:04 p.m.