Triple

T23839804
Position Surface form Disambiguated ID Type / Status
Subject Unicode Line Breaking Algorithm E590953 entity
Predicate usedBy P260 FINISHED
Object CSS line-breaking properties
CSS line-breaking properties are CSS features that control how and where lines of text wrap in web content, following rules such as those defined by the Unicode Line Breaking Algorithm.
E1604549 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: CSS line-breaking properties | Statement: [Unicode Line Breaking Algorithm, usedBy, CSS line-breaking properties]
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: CSS line-breaking properties
Triple: [Unicode Line Breaking Algorithm, usedBy, CSS line-breaking properties]
Generated description
CSS line-breaking properties are CSS features that control how and where lines of text wrap in web content, following rules such as those defined by the Unicode Line Breaking Algorithm.

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_69e25d1de32c8190a907afe9c3d6cd6d completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1c886aab081909ad896abe383afb9 completed April 29, 2026, 8:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f69a70c6c8190a052975577170056 completed May 21, 2026, 8:23 p.m.
NEDg Description generation batch_6a0f6d40d1108190b4da250e40014008 completed May 21, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0f6e22305081909ad33dfaf65f004e completed May 21, 2026, 8:42 p.m.
Created at: April 17, 2026, 8:08 p.m.