Triple
T3704465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Paso International Airport |
E80857
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
KELP
KELP is the ICAO airport code for El Paso International Airport, a commercial airport serving El Paso, Texas.
|
E382997
|
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: KELP | Statement: [El Paso International Airport, ICAOcode, KELP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KELP Context triple: [El Paso International Airport, ICAOcode, KELP]
-
A.
Kee
Kee is a pivotal character in the dystopian film "Children of Men," a miraculously pregnant refugee whose unborn child represents humanity’s last hope for survival.
-
B.
Kileler
Kileler is a municipality and village in the Thessaly region of central Greece, known historically for its agricultural character and the 1910 peasant uprising.
-
C.
Kaiyukan
Kaiyukan is a large, world-renowned public aquarium in Osaka, Japan, famous for its massive central tank and immersive marine life exhibits.
-
D.
Taroa
Taroa is the main settlement and administrative center of Maloelap Atoll in the Marshall Islands, known historically for its World War II-era Japanese airbase.
-
E.
Kaul
Kaul is a Kashmiri Pandit surname historically associated with prominent Indian families, including that of Kamala Nehru.
- 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: KELP Triple: [El Paso International Airport, ICAOcode, KELP]
Generated description
KELP is the ICAO airport code for El Paso International Airport, a commercial airport serving El Paso, Texas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KELP Target entity description: KELP is the ICAO airport code for El Paso International Airport, a commercial airport serving El Paso, Texas.
-
A.
Kee
Kee is a pivotal character in the dystopian film "Children of Men," a miraculously pregnant refugee whose unborn child represents humanity’s last hope for survival.
-
B.
Kileler
Kileler is a municipality and village in the Thessaly region of central Greece, known historically for its agricultural character and the 1910 peasant uprising.
-
C.
Kaiyukan
Kaiyukan is a large, world-renowned public aquarium in Osaka, Japan, famous for its massive central tank and immersive marine life exhibits.
-
D.
Taroa
Taroa is the main settlement and administrative center of Maloelap Atoll in the Marshall Islands, known historically for its World War II-era Japanese airbase.
-
E.
Kaul
Kaul is a Kashmiri Pandit surname historically associated with prominent Indian families, including that of Kamala Nehru.
- 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_69ad8b1793888190a5f70e4b21dc05a1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc54aaac88190b775dba2513b6d4a |
completed | March 8, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4cdfada708190ba6ae04bad52bb2b |
completed | March 14, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69b4d2be3be4819082c9627099b78e32 |
completed | March 14, 2026, 3:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4d395336881908a942dd408560a81 |
completed | March 14, 2026, 3:18 a.m. |
Created at: March 8, 2026, 3:33 p.m.