Triple

T33904653
Position Surface form Disambiguated ID Type / Status
Subject Sylvia May Laura Syms E869147 entity
Predicate notableWork P4 FINISHED
Object No Trees in the Street
No Trees in the Street is a 1959 British crime drama film set in a poverty-stricken London slum, exploring themes of hardship and criminal temptation.
 E2072629 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: No Trees in the Street | Statement: [Sylvia May Laura Syms, notableWork, No Trees in the Street]
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: No Trees in the Street
Triple: [Sylvia May Laura Syms, notableWork, No Trees in the Street]
Generated description
No Trees in the Street is a 1959 British crime drama film set in a poverty-stricken London slum, exploring themes of hardship and criminal temptation.

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_69f34997703c8190866b1d404bce531f completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70185c9b0819086578ffc61822d36 completed May 3, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a368245e0588190a71829ba452c622e completed June 20, 2026, 12:06 p.m.
NEDg Description generation batch_6a3682cb71688190a91c1b9ecba2c37f completed June 20, 2026, 12:08 p.m.
NED2 Entity disambiguation (via description) batch_6a368327e7248190801ee93ba760d704 completed June 20, 2026, 12:10 p.m.
Created at: May 1, 2026, 1:48 a.m.