Triple
T16069545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East London Airport |
E389823
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
ELS
ELS is the IATA airport code for East London Airport, a regional airport serving the city of East London in South Africa.
|
E1192913
|
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: ELS | Statement: [East London Airport, IATAcode, ELS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ELS Context triple: [East London Airport, IATAcode, ELS]
-
A.
ELS
ELS is the abbreviation for the Existing Liabilities Scheme, a regulatory framework dealing with pre-existing financial or insurance obligations.
-
B.
ELS
ELS is a Soyuz launch complex at the Guiana Space Centre in French Guiana used for orbiting satellites and other payloads.
-
C.
ESS
ESS is the commonly used abbreviation for the European Standardization System, the framework through which European standards are developed and harmonized.
-
D.
ESS
ESS is the commonly used abbreviation for Étoile Sportive du Sahel, a prominent multi-sport club based in Sousse, Tunisia, best known for its successful football team.
-
E.
ERS
ERS is a hybrid Formula 1 power unit component that recovers and stores energy from braking and exhaust heat to provide additional electrical power for improved performance and efficiency.
- 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: ELS Triple: [East London Airport, IATAcode, ELS]
Generated description
ELS is the IATA airport code for East London Airport, a regional airport serving the city of East London in South Africa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ELS Target entity description: ELS is the IATA airport code for East London Airport, a regional airport serving the city of East London in South Africa.
-
A.
ELS
ELS is the abbreviation for the Existing Liabilities Scheme, a regulatory framework dealing with pre-existing financial or insurance obligations.
-
B.
ELS
ELS is a Soyuz launch complex at the Guiana Space Centre in French Guiana used for orbiting satellites and other payloads.
-
C.
ESS
ESS is the commonly used abbreviation for the European Standardization System, the framework through which European standards are developed and harmonized.
-
D.
ESS
ESS is the commonly used abbreviation for Étoile Sportive du Sahel, a prominent multi-sport club based in Sousse, Tunisia, best known for its successful football team.
-
E.
ERS
ERS is a hybrid Formula 1 power unit component that recovers and stores energy from braking and exhaust heat to provide additional electrical power for improved performance and efficiency.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183bd9578819097e7cb1108b1f6f7 |
completed | April 17, 2026, 12:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe4827cd48190aa470c6537e72508 |
completed | May 10, 2026, 1:50 a.m. |
| NEDg | Description generation | batch_69ffe6c956a48190845faac983b9a064 |
completed | May 10, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffe7387b38819094e55ae14ec2c036 |
completed | May 10, 2026, 2:02 a.m. |
Created at: April 10, 2026, 4:57 a.m.