Triple
T16541797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wendover Airfield |
E401834
|
entity |
| Predicate | hasFaaCode |
P420
|
FINISHED |
| Object |
ENV
ENV is the FAA airport code for Wendover Airfield, a public airport serving Wendover, Utah.
|
E1219928
|
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: ENV | Statement: [Wendover Airfield, hasFaaCode, ENV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ENV Context triple: [Wendover Airfield, hasFaaCode, ENV]
-
A.
ENV
ENV is the College of Environmental Design at California State Polytechnic University, Pomona, which focuses on disciplines such as architecture, landscape architecture, and urban and regional planning.
-
B.
ENVA
ENVA is the ICAO airport code for Trondheim Airport, Værnes, a major international airport serving the Trondheim region in Norway.
-
C.
ENBR
ENBR is the ICAO airport code for Bergen Airport, Flesland, the main international airport serving Bergen, Norway.
-
D.
Env API
Env API is the core interface specification in OpenAI Gym that standardizes how reinforcement learning environments interact with agents through methods like reset, step, and render.
-
E.
ENVD
ENVD is the ICAO airport code for Vadsø Airport, a regional airport serving the town of Vadsø in northern Norway.
- 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: ENV Triple: [Wendover Airfield, hasFaaCode, ENV]
Generated description
ENV is the FAA airport code for Wendover Airfield, a public airport serving Wendover, Utah.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ENV Target entity description: ENV is the FAA airport code for Wendover Airfield, a public airport serving Wendover, Utah.
-
A.
ENV
ENV is the College of Environmental Design at California State Polytechnic University, Pomona, which focuses on disciplines such as architecture, landscape architecture, and urban and regional planning.
-
B.
ENVA
ENVA is the ICAO airport code for Trondheim Airport, Værnes, a major international airport serving the Trondheim region in Norway.
-
C.
ENBR
ENBR is the ICAO airport code for Bergen Airport, Flesland, the main international airport serving Bergen, Norway.
-
D.
Env API
Env API is the core interface specification in OpenAI Gym that standardizes how reinforcement learning environments interact with agents through methods like reset, step, and render.
-
E.
ENVD
ENVD is the ICAO airport code for Vadsø Airport, a regional airport serving the town of Vadsø in northern Norway.
- 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3455db6788190b929546050ea2488 |
completed | April 18, 2026, 8:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0067b0e5708190a286b8a316d6efd2 |
completed | May 10, 2026, 11:10 a.m. |
| NEDg | Description generation | batch_6a006895b8ac8190a8d078e6b9f5bb50 |
completed | May 10, 2026, 11:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00694da4a88190944ae4a70ac9f0c3 |
completed | May 10, 2026, 11:17 a.m. |
Created at: April 10, 2026, 5:15 a.m.