Triple
T7008027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imperial County Airport |
E162507
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
KIPL
KIPL is the ICAO airport code for Imperial County Airport, a public airport serving the Imperial County region in California, United States.
|
E635798
|
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: KIPL | Statement: [Imperial County Airport, ICAOcode, KIPL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KIPL Context triple: [Imperial County Airport, ICAOcode, KIPL]
-
A.
KI
KI is the regional vehicle registration code used on license plates to identify vehicles registered in Kyiv Oblast, Ukraine.
-
B.
KI
KI is the vehicle registration code used on license plates for the German city of Kiel.
-
C.
KI
KI is the abbreviation for the Karolinska Institute, a renowned Swedish medical university known for its leading research and role in selecting Nobel laureates in Physiology or Medicine.
-
D.
KLu
KLu is the commonly used abbreviation for the Royal Netherlands Air Force, the aerial warfare branch of the Dutch armed forces.
-
E.
Kip
Kip is a young Sikh British-Indian army sapper in Michael Ondaatje’s novel "The English Patient," whose expertise in bomb disposal and complex relationship with the other characters explore themes of war, identity, and colonialism.
- 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: KIPL Triple: [Imperial County Airport, ICAOcode, KIPL]
Generated description
KIPL is the ICAO airport code for Imperial County Airport, a public airport serving the Imperial County region in California, United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KIPL Target entity description: KIPL is the ICAO airport code for Imperial County Airport, a public airport serving the Imperial County region in California, United States.
-
A.
KI
KI is the regional vehicle registration code used on license plates to identify vehicles registered in Kyiv Oblast, Ukraine.
-
B.
KI
KI is the vehicle registration code used on license plates for the German city of Kiel.
-
C.
KI
KI is the abbreviation for the Karolinska Institute, a renowned Swedish medical university known for its leading research and role in selecting Nobel laureates in Physiology or Medicine.
-
D.
KLu
KLu is the commonly used abbreviation for the Royal Netherlands Air Force, the aerial warfare branch of the Dutch armed forces.
-
E.
Kip
Kip is a young Sikh British-Indian army sapper in Michael Ondaatje’s novel "The English Patient," whose expertise in bomb disposal and complex relationship with the other characters explore themes of war, identity, and colonialism.
- 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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc36e3fc8190957445132a9ffb5f |
completed | March 27, 2026, 7:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a43c3a081909b9150d36ba107f5 |
completed | March 28, 2026, 5:42 a.m. |
| NEDg | Description generation | batch_69c76b3c87708190a04c48c41bb9904b |
completed | March 28, 2026, 5:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c76bb8c7788190bf54b805f651e28e |
completed | March 28, 2026, 5:48 a.m. |
Created at: March 27, 2026, 2:33 p.m.