Triple
T12871604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air Algérie |
E307862
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object | DAH |
E794717
|
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: DAH | Statement: [Air Algérie, ICAOcode, DAH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DAH Context triple: [Air Algérie, ICAOcode, DAH]
-
A.
DAH
chosen
DAH is the vehicle registration code used for motor vehicles registered in the rural district of Dachau in Bavaria, Germany.
-
B.
DAR
DAR is the Philippine government agency responsible for implementing agrarian reform and redistributing agricultural land to farmers.
-
C.
DA
DA is a postcode area in southeast England covering parts of south-east London and northwest Kent, including towns such as Dartford and Sidcup.
-
D.
DA
DA is the vehicle registration code for the German city of Darmstadt and its surrounding district in the state of Hesse.
-
E.
DA
DA is the commonly used abbreviation for the Defence Academy of the United Kingdom, the institution responsible for advanced education and training of the UK’s armed forces and defence personnel.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970905784819091631161a9de98c5 |
completed | April 10, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69bb4b28c8190a4ec9cad4e1e0f05 |
completed | May 3, 2026, 12:49 a.m. |
Created at: April 9, 2026, 5:38 p.m.