Triple

T36659613
Position Surface form Disambiguated ID Type / Status
Subject Marikina Valley E905084 entity
Predicate majorFloodEvents P39938 FINISHED
Object Typhoon Ulysses (Vamco) 2020
Typhoon Ulysses (Vamco) 2020 was a powerful and destructive tropical cyclone that struck the Philippines, causing severe flooding—particularly in Metro Manila and surrounding areas—and resulting in widespread damage and casualties.
E2193637 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: Typhoon Ulysses (Vamco) 2020 | Statement: [Marikina Valley, majorFloodEvents, Typhoon Ulysses (Vamco) 2020]
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: Typhoon Ulysses (Vamco) 2020
Triple: [Marikina Valley, majorFloodEvents, Typhoon Ulysses (Vamco) 2020]
Generated description
Typhoon Ulysses (Vamco) 2020 was a powerful and destructive tropical cyclone that struck the Philippines, causing severe flooding—particularly in Metro Manila and surrounding areas—and resulting in widespread damage and casualties.

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_69f76e6e3b908190970251b30f76ad71 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69ffc57580088190bf0d41dbac0eb556 completed May 9, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3a20d0d3808190bf7fbfc9f41df794 completed June 23, 2026, 5:59 a.m.
NEDg Description generation batch_6a3a220ec9088190b62d38586312d0e9 completed June 23, 2026, 6:05 a.m.
NED2 Entity disambiguation (via description) batch_6a3a227037a08190813771104b9abed5 completed June 23, 2026, 6:06 a.m.
Created at: May 3, 2026, 4:11 p.m.