Triple
T15284685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gulfport–Biloxi International Airport |
E365363
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
GPT
GPT is the IATA airport code for Gulfport–Biloxi International Airport in Gulfport, Mississippi, United States.
|
E1148332
|
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: GPT | Statement: [Gulfport–Biloxi International Airport, IATAcode, GPT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GPT Context triple: [Gulfport–Biloxi International Airport, IATAcode, GPT]
-
A.
GPT
GPT is a family of large language models developed by OpenAI that can understand and generate human-like text for a wide range of tasks.
-
B.
GPT
GPT (GUID Partition Table) is a modern disk partitioning scheme that supports large drives, many partitions, and improved reliability compared to older MBR- and APM-based systems.
-
C.
ChatGPT
ChatGPT is an advanced conversational AI model developed by OpenAI that can understand and generate human-like text across a wide range of topics and tasks.
-
D.
GPT-3.5
GPT-3.5 is a large language model that generates human-like text and powers conversational AI applications such as advanced chatbots and coding assistants.
-
E.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
- 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: GPT Triple: [Gulfport–Biloxi International Airport, IATAcode, GPT]
Generated description
GPT is the IATA airport code for Gulfport–Biloxi International Airport in Gulfport, Mississippi, United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: GPT Target entity description: GPT is the IATA airport code for Gulfport–Biloxi International Airport in Gulfport, Mississippi, United States.
-
A.
GPT
GPT is a family of large language models developed by OpenAI that can understand and generate human-like text for a wide range of tasks.
-
B.
GPT
GPT (GUID Partition Table) is a modern disk partitioning scheme that supports large drives, many partitions, and improved reliability compared to older MBR- and APM-based systems.
-
C.
ChatGPT
ChatGPT is an advanced conversational AI model developed by OpenAI that can understand and generate human-like text across a wide range of topics and tasks.
-
D.
GPT-3.5
GPT-3.5 is a large language model that generates human-like text and powers conversational AI applications such as advanced chatbots and coding assistants.
-
E.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e53c9588190a6cb61ac8805c706 |
completed | April 15, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef798a588190981c77e6f4c6be78 |
completed | May 9, 2026, 8:25 a.m. |
| NEDg | Description generation | batch_69fef1c3c054819096b1cf2e7887be49 |
completed | May 9, 2026, 8:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fef24136c08190add6cbe1c6b2c0e2 |
completed | May 9, 2026, 8:37 a.m. |
Created at: April 10, 2026, 3:15 a.m.