Triple
T4384587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assiut Airport |
E99209
|
entity |
| Predicate | pushpinLabel |
P9248
|
FINISHED |
| Object | ATZ |
E436504
|
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: ATZ | Statement: [Assiut Airport, pushpinLabel, ATZ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ATZ Context triple: [Assiut Airport, pushpinLabel, ATZ]
-
A.
ATZ
chosen
ATZ is the IATA airport code for Assiut Airport, a regional airport serving the city of Assiut in Egypt.
-
B.
ATN
ATN most likely refers to Augmented Transition Network, a type of finite state machine used in computational linguistics and natural language processing for parsing sentences.
-
C.
AZU
AZU is the ICAO airline designator for Azul Brazilian Airlines, a major low-cost carrier based in Brazil.
-
D.
ATAS
ATAS is the commonly used acronym for the Academy of Television Arts & Sciences, the organization best known for administering the Primetime Emmy Awards.
-
E.
AT4
AT4 is an off-road-focused trim level of the GMC Sierra pickup truck, featuring enhanced suspension, rugged styling, and all-terrain capability.
- 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35263970c8190904ee20d81715833 |
completed | March 12, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f5e74ba481908876629c811d934f |
completed | March 14, 2026, 11:57 p.m. |
Created at: March 12, 2026, 11:19 p.m.