Triple

T26467903
Position Surface form Disambiguated ID Type / Status
Subject Río Duero E665816 entity
Predicate locatedNear P294 FINISHED
Object municipality of Tangancícuaro
The municipality of Tangancícuaro is a local administrative region in the Mexican state of Michoacán, known for its agricultural activities and proximity to rivers and natural water bodies.
E1725509 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: municipality of Tangancícuaro | Statement: [Río Duero, locatedNear, municipality of Tangancícuaro]
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: municipality of Tangancícuaro
Triple: [Río Duero, locatedNear, municipality of Tangancícuaro]
Generated description
The municipality of Tangancícuaro is a local administrative region in the Mexican state of Michoacán, known for its agricultural activities and proximity to rivers and natural water bodies.

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_69ee883f80dc819090e311b022b78e02 completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f612c5f208819096a791c6a90dc571 completed May 2, 2026, 3:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11aee1665081908c35073e6532903a completed May 23, 2026, 1:42 p.m.
NEDg Description generation batch_6a11b01df9348190a991161aa1fb8018 completed May 23, 2026, 1:48 p.m.
NED2 Entity disambiguation (via description) batch_6a11b1230c148190931c49c9df40caa4 completed May 23, 2026, 1:52 p.m.
Created at: April 27, 2026, 12:17 a.m.