Triple

T25824190
Position Surface form Disambiguated ID Type / Status
Subject San Lázaro metro station E650481 entity
Predicate fareSystem P395 FINISHED
Object Mexico City Metro fare system
The Mexico City Metro fare system is the unified ticketing and payment structure that governs how passengers pay and validate rides across the Mexico City Metro network.
E814296 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: Mexico City Metro fare system | Statement: [San Lázaro metro station, fareSystem, Mexico City Metro fare system]
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: Mexico City Metro fare system
Triple: [San Lázaro metro station, fareSystem, Mexico City Metro fare system]
Generated description
The Mexico City Metro fare system is the unified ticketing and payment structure that governs how passengers pay and validate rides across the Mexico City Metro network.

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_69e7ab367fcc8190a5ff1e7f3da046a4 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f6019375888190a8f71cc7a978a3b7 completed May 2, 2026, 1:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10da2634bc8190acf522c2ed88cac5 completed May 22, 2026, 10:35 p.m.
NEDg Description generation batch_6a10db8c106c8190b80bae3db0d75e67 completed May 22, 2026, 10:41 p.m.
NED2 Entity disambiguation (via description) batch_6a10dc22616081909237e90fee63a70d completed May 22, 2026, 10:43 p.m.
Created at: April 22, 2026, 7:31 a.m.