Triple

T24180819
Position Surface form Disambiguated ID Type / Status
Subject First Lady of Puerto Rico E599417 entity
Predicate positionHeldBy P8 FINISHED
Object María Elena González
María Elena González is a Puerto Rican public figure who served as First Lady of Puerto Rico, engaging in social, cultural, and charitable initiatives during her tenure.
E1852358 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: María Elena González | Statement: [First Lady of Puerto Rico, positionHeldBy, María Elena González]
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: María Elena González
Triple: [First Lady of Puerto Rico, positionHeldBy, María Elena González]
Generated description
María Elena González is a Puerto Rican public figure who served as First Lady of Puerto Rico, engaging in social, cultural, and charitable initiatives during her tenure.

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_69e288cca05481908faeb1563711114a completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1e1d4ef208190849d4ba1351fcb0f completed April 29, 2026, 10:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2550275dfc8190a49811931904c7f6 completed June 7, 2026, 11:04 a.m.
NEDg Description generation batch_6a2554568f388190960bbfb09ed37b1d completed June 7, 2026, 11:21 a.m.
NED2 Entity disambiguation (via description) batch_6a2558e69dfc81908eea54a231ab38e7 completed June 7, 2026, 11:41 a.m.
Created at: April 17, 2026, 11:34 p.m.