Triple

T8080100
Position Surface form Disambiguated ID Type / Status
Subject Net 25 E188591 entity
Predicate hasProgram P178 FINISHED
Object Tadhana
Tadhana is a Filipino television drama anthology series that features real-life stories of overseas Filipino workers and other inspiring narratives.
 E713432 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: Tadhana | Statement: [Net 25, hasProgram, Tadhana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tadhana
Context triple: [Net 25, hasProgram, Tadhana]
  • A. Minalin
    Minalin is a municipality in the province of Pampanga in the Philippines, known for its agricultural economy and traditional cultural festivities.
  • B. Mabini
    Mabini is a coastal municipality in the province of Batangas in the Philippines, known for its diving spots and marine biodiversity.
  • C. Maragondon
    Maragondon is a historic rural municipality in the province of Cavite in the Philippines, known for its Spanish-era heritage sites and nearby natural attractions.
  • D. Sulat
    Sulat is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and Pacific shoreline.
  • E. Kalamansig
    Kalamansig is a coastal municipality in the province of Sultan Kudarat in the Philippines, known for its fishing industry and diverse indigenous communities.
  • 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: Tadhana
Triple: [Net 25, hasProgram, Tadhana]
Generated description
Tadhana is a Filipino television drama anthology series that features real-life stories of overseas Filipino workers and other inspiring narratives.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tadhana
Target entity description: Tadhana is a Filipino television drama anthology series that features real-life stories of overseas Filipino workers and other inspiring narratives.
  • A. Minalin
    Minalin is a municipality in the province of Pampanga in the Philippines, known for its agricultural economy and traditional cultural festivities.
  • B. Mabini
    Mabini is a coastal municipality in the province of Batangas in the Philippines, known for its diving spots and marine biodiversity.
  • C. Maragondon
    Maragondon is a historic rural municipality in the province of Cavite in the Philippines, known for its Spanish-era heritage sites and nearby natural attractions.
  • D. Sulat
    Sulat is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and Pacific shoreline.
  • E. Kalamansig
    Kalamansig is a coastal municipality in the province of Sultan Kudarat in the Philippines, known for its fishing industry and diverse indigenous communities.
  • 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_69ca82b662e88190b9323daab8c28a21 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb40a504d48190ace96e814d99b182 completed March 31, 2026, 3:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc93eddef48190b5f499a5b52428c8 completed April 1, 2026, 3:41 a.m.
NEDg Description generation batch_69cc9557c6148190a759021b6add0a61 completed April 1, 2026, 3:47 a.m.
NED2 Entity disambiguation (via description) batch_69cc96a8bb688190a352de1798b380f1 completed April 1, 2026, 3:53 a.m.
Created at: March 30, 2026, 5:28 p.m.