Triple

T24144125
Position Surface form Disambiguated ID Type / Status
Subject Mount Isarog E598326 entity
Predicate protectedAreaDesignation P3029 FINISHED
Object Mount Isarog National Park
Mount Isarog National Park is a protected natural area on the slopes of Mount Isarog in the Philippines, known for its rich biodiversity, forested landscapes, and hiking trails.
E1626100 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: Mount Isarog National Park | Statement: [Mount Isarog, protectedAreaDesignation, Mount Isarog National Park]
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: Mount Isarog National Park
Triple: [Mount Isarog, protectedAreaDesignation, Mount Isarog National Park]
Generated description
Mount Isarog National Park is a protected natural area on the slopes of Mount Isarog in the Philippines, known for its rich biodiversity, forested landscapes, and hiking trails.

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_69e288c9e488819093dd1acd91b08b8a completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1e008efbc8190ac6c12d3ba5dd5d8 completed April 29, 2026, 10:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fbd061520819091366de6290e7b26 completed May 22, 2026, 2:18 a.m.
NEDg Description generation batch_6a0fc0ac654c81908e3b8af4d47b0245 completed May 22, 2026, 2:34 a.m.
NED2 Entity disambiguation (via description) batch_6a0fc17855cc8190b4a353b7e94fa0c3 completed May 22, 2026, 2:37 a.m.
Created at: April 17, 2026, 11:29 p.m.