Triple
T26620158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buin District |
E668170
|
entity |
| Predicate | hasTransportInfrastructure |
P2560
|
FINISHED |
| Object |
Buin Airport
Buin Airport is a small regional airfield serving the Buin area in the Autonomous Region of Bougainville, Papua New Guinea.
|
E1733839
|
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: Buin Airport | Statement: [Buin District, hasTransportInfrastructure, Buin Airport]
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: Buin Airport Triple: [Buin District, hasTransportInfrastructure, Buin Airport]
Generated description
Buin Airport is a small regional airfield serving the Buin area in the Autonomous Region of Bougainville, Papua New Guinea.
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_69ee9cfe16088190a3dddd68e3c7b1ea |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f615b0350481908c119c74dc0a4998 |
completed | May 2, 2026, 3:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11ec2cba888190a92d85ed72d1465a |
completed | May 23, 2026, 6:04 p.m. |
| NEDg | Description generation | batch_6a11ecd9cd6c819081708a8ccf46b3ab |
completed | May 23, 2026, 6:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a11ed798a9c8190a3f3af5b000b0dcb |
completed | May 23, 2026, 6:10 p.m. |
Created at: April 27, 2026, 2:20 a.m.