Triple

T4536725
Position Surface form Disambiguated ID Type / Status
Subject Makati E107424 entity
Predicate hasDistrict P459 FINISHED
Object Cembo
Cembo is a residential and commercial barangay in Makati City, Philippines, known for its dense urban community and proximity to major business districts.
 E450367 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: Cembo | Statement: [Makati, hasDistrict, Cembo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cembo
Context triple: [Makati, hasDistrict, Cembo]
  • A. Yambu
    Yambu is a coastal city in western Saudi Arabia on the Red Sea, known as an important port and industrial center.
  • B. Tamambo
    Tamambo is an Oceanic Austronesian language spoken primarily on Malo Island in Vanuatu.
  • C. Zemba
    Zemba is a Bantu language variety spoken primarily in southwestern Angola and northern Namibia, closely related to and often considered a dialect of Herero.
  • D. Chomu
    Chomu is a historic town in the Indian state of Rajasthan, known for its traditional architecture and cultural heritage.
  • E. Chimbo
    Chimbo is a small town in central Ecuador known for its colonial heritage and location within Bolívar Province in the Andean highlands.
  • 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: Cembo
Triple: [Makati, hasDistrict, Cembo]
Generated description
Cembo is a residential and commercial barangay in Makati City, Philippines, known for its dense urban community and proximity to major business districts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cembo
Target entity description: Cembo is a residential and commercial barangay in Makati City, Philippines, known for its dense urban community and proximity to major business districts.
  • A. Yambu
    Yambu is a coastal city in western Saudi Arabia on the Red Sea, known as an important port and industrial center.
  • B. Tamambo
    Tamambo is an Oceanic Austronesian language spoken primarily on Malo Island in Vanuatu.
  • C. Zemba
    Zemba is a Bantu language variety spoken primarily in southwestern Angola and northern Namibia, closely related to and often considered a dialect of Herero.
  • D. Chomu
    Chomu is a historic town in the Indian state of Rajasthan, known for its traditional architecture and cultural heritage.
  • E. Chimbo
    Chimbo is a small town in central Ecuador known for its colonial heritage and location within Bolívar Province in the Andean highlands.
  • 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_69bd43f922788190b7edfa294e39b178 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57b78b8481909d79131723d4be22 completed March 20, 2026, 2:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdacf5858081909d38cad86d4014f2 completed March 20, 2026, 8:24 p.m.
NEDg Description generation batch_69bdad97c9e0819093849c81fb2a3d8c completed March 20, 2026, 8:27 p.m.
NED2 Entity disambiguation (via description) batch_69bdae2dd51081909a24017a4b983f70 completed March 20, 2026, 8:29 p.m.
Created at: March 20, 2026, 1:04 p.m.