Triple
T15243642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mo i Rana Airport, Røssvoll |
E364320
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
ENRA
ENRA is the ICAO airport code for Mo i Rana Airport, Røssvoll in Norway.
|
E1145507
|
NE FINISHED |
How this triple was built (4 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: ENRA | Statement: [Mo i Rana Airport, Røssvoll, ICAOcode, ENRA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ENRA Context triple: [Mo i Rana Airport, Røssvoll, ICAOcode, ENRA]
-
A.
ENRD
ENRD is the Environment and Natural Resources Division of the U.S. Department of Justice, responsible for litigating environmental and natural resource-related cases on behalf of the federal government.
-
B.
ENBR
ENBR is the ICAO airport code for Bergen Airport, Flesland, the main international airport serving Bergen, Norway.
-
C.
ENAS
ENAS (Efficient Neural Architecture Search) is a method that dramatically reduces the computational cost of neural architecture search by sharing parameters among many candidate architectures within a single super-network.
-
D.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
-
E.
ENJA
ENJA is the ICAO airport code for Jan Mayensfield, the airfield serving the remote Norwegian island of Jan Mayen in the Arctic Ocean.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ENRA Triple: [Mo i Rana Airport, Røssvoll, ICAOcode, ENRA]
Generated description
ENRA is the ICAO airport code for Mo i Rana Airport, Røssvoll in Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ENRA Target entity description: ENRA is the ICAO airport code for Mo i Rana Airport, Røssvoll in Norway.
-
A.
ENRD
ENRD is the Environment and Natural Resources Division of the U.S. Department of Justice, responsible for litigating environmental and natural resource-related cases on behalf of the federal government.
-
B.
ENBR
ENBR is the ICAO airport code for Bergen Airport, Flesland, the main international airport serving Bergen, Norway.
-
C.
ENAS
ENAS (Efficient Neural Architecture Search) is a method that dramatically reduces the computational cost of neural architecture search by sharing parameters among many candidate architectures within a single super-network.
-
D.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
-
E.
ENJA
ENJA is the ICAO airport code for Jan Mayensfield, the airfield serving the remote Norwegian island of Jan Mayen in the Arctic Ocean.
- F. None of above. chosen
Provenance (5 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007dcc33081908545ea1a1d2c19fe |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd461cf08190a506aac2f0cec83a |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fedf6ee3f081909553078cd3e9d243 |
completed | May 9, 2026, 7:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fee0016a088190ad87268e035f677e |
completed | May 9, 2026, 7:19 a.m. |
Created at: April 10, 2026, 3:13 a.m.