Triple
T15693957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alta Airport |
E380406
|
entity |
| Predicate | ICAO code |
P419
|
FINISHED |
| Object |
ENAT
ENAT is the ICAO airport code for Alta Airport, a regional airport in Alta, Norway.
|
E1171108
|
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: ENAT | Statement: [Alta Airport, ICAO code, ENAT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ENAT Context triple: [Alta Airport, ICAO code, ENAT]
-
A.
ENAT
ENAT is Mexico’s National School of Theatrical Arts, a leading institution dedicated to professional training in theater and performance.
-
B.
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.
-
C.
ENRA
ENRA is the ICAO airport code for Mo i Rana Airport, Røssvoll in Norway.
-
D.
ENEA
ENEA is Italy’s national agency for new technologies, energy, and sustainable economic development, focused on research and innovation in fields such as energy, environment, and advanced technologies.
-
E.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
- 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: ENAT Triple: [Alta Airport, ICAO code, ENAT]
Generated description
ENAT is the ICAO airport code for Alta Airport, a regional airport in Alta, Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ENAT Target entity description: ENAT is the ICAO airport code for Alta Airport, a regional airport in Alta, Norway.
-
A.
ENAT
ENAT is Mexico’s National School of Theatrical Arts, a leading institution dedicated to professional training in theater and performance.
-
B.
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.
-
C.
ENRA
ENRA is the ICAO airport code for Mo i Rana Airport, Røssvoll in Norway.
-
D.
ENEA
ENEA is Italy’s national agency for new technologies, energy, and sustainable economic development, focused on research and innovation in fields such as energy, environment, and advanced technologies.
-
E.
ENA
ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
- 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f4f5a888190bd3681bcb9bbc02f |
completed | April 16, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6eed9a8c8190a57ffce61a27ec17 |
completed | May 9, 2026, 5:29 p.m. |
| NEDg | Description generation | batch_69ff7097adec8190ad5fc00fa57e3383 |
completed | May 9, 2026, 5:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff70f97eec8190a1f5affdad31f2b2 |
completed | May 9, 2026, 5:38 p.m. |
Created at: April 10, 2026, 4:44 a.m.