Triple

T26741259
Position Surface form Disambiguated ID Type / Status
Subject Hongshui River E674264 entity
Predicate nameMeaning P453 FINISHED
Object Red Water River
Red Water River is the English meaning of the name "Hongshui River," a major river in southern China known for its reddish, sediment-rich waters.
 E1799937 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: Red Water River | Statement: [Hongshui River, nameMeaning, Red Water River]
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: Red Water River
Triple: [Hongshui River, nameMeaning, Red Water River]
Generated description
Red Water River is the English meaning of the name "Hongshui River," a major river in southern China known for its reddish, sediment-rich waters.

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_69eecda63a3881908095c47900692e65 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f6187e939c81908e5da8b43227a444 completed May 2, 2026, 3:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15b86653348190b8b883db2060a976 completed May 26, 2026, 3:12 p.m.
NEDg Description generation batch_6a15bca032408190a0f3fbee5e29cbf8 completed May 26, 2026, 3:30 p.m.
NED2 Entity disambiguation (via description) batch_6a15bd0bb30c8190b4cce0b94f4efcac completed May 26, 2026, 3:32 p.m.
Created at: April 27, 2026, 3:49 a.m.