Triple

T24840553

Position	Surface form	Disambiguated ID	Type / Status
Subject	Secretariat of Social Development of Mexico	`E621601`	entity
Predicate	operatedProgram	`P146254`	FINISHED
Object	Oportunidades Oportunidades was a major Mexican conditional cash transfer program aimed at reducing poverty by providing financial support to low-income families in exchange for meeting health and education requirements.	`E1654236`	NE FINISHED

How this triple was built (3 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: Oportunidades | Statement: [Secretariat of Social Development of Mexico, operatedProgram, Oportunidades]

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: Oportunidades
Triple: [Secretariat of Social Development of Mexico, operatedProgram, Oportunidades]

Generated description

Oportunidades was a major Mexican conditional cash transfer program aimed at reducing poverty by providing financial support to low-income families in exchange for meeting health and education requirements.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: operatedProgram
Context triple: [Secretariat of Social Development of Mexico, operatedProgram, Oportunidades]

A. programOperation
Indicates an operation or action performed by, within, or upon a program in a computational or procedural context.
B. servedProgram chosen
Indicates that an entity has provided, delivered, or made available a particular program to another party or context.
C. intendedProgram
Indicates the academic or training program that an entity plans or expects to pursue, rather than one they are currently enrolled in or have completed.
D. userProgram
Indicates that a user is associated with, uses, or is responsible for a particular program.
E. programOn
Indicates that a program is scheduled to be broadcast or presented on a particular channel, station, platform, or device.
F. None of above.

Provenance (6 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_69e2fac185d48190a0a6073ad1f6b792`	completed	April 18, 2026, 3:30 a.m.
NER	Named-entity recognition	`batch_69f638d11c988190af7fd4572b08e038`	completed	May 2, 2026, 5:48 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a101c4d268081908fa119333d0ec727`	completed	May 22, 2026, 9:05 a.m.
NEDg	Description generation	`batch_6a102814f838819094ed41d653039f72`	completed	May 22, 2026, 9:55 a.m.
NED2	Entity disambiguation (via description)	`batch_6a10294485508190a91d9ec391181047`	completed	May 22, 2026, 10 a.m.
PD	Predicate disambiguation	`batch_69f63706b6008190993577193c85ff50`	completed	May 2, 2026, 5:40 p.m.

Created at: April 18, 2026, 5:18 a.m.