Triple

T24593784
Position Surface form Disambiguated ID Type / Status
Subject Lapuyan Subanen E608608 entity
Predicate spokenIn P2266 FINISHED
Object Lapuyan
Lapuyan is a municipality in the province of Zamboanga del Sur in the Philippines, known as a traditional homeland of the Subanen people.
E1646072 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: Lapuyan | Statement: [Lapuyan Subanen, spokenIn, Lapuyan]
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: Lapuyan
Triple: [Lapuyan Subanen, spokenIn, Lapuyan]
Generated description
Lapuyan is a municipality in the province of Zamboanga del Sur in the Philippines, known as a traditional homeland of the Subanen people.

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_69e2c4cf54248190af7b0c2d9ade9830 completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a9dd66d081909e99a04a2a96fba8 completed April 30, 2026, 1:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100fe962c88190845446dde7153c04 completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10109202f08190a6a9c81820800785 completed May 22, 2026, 8:15 a.m.
NED2 Entity disambiguation (via description) batch_6a10137faa288190ba9e17f59e14d4d3 completed May 22, 2026, 8:27 a.m.
Created at: April 18, 2026, 2:30 a.m.