Triple

T29542493
Position Surface form Disambiguated ID Type / Status
Subject In the Mood for Love E749541 entity
Predicate mainCharacter P1183 FINISHED
Object Su Li-zhen
Su Li-zhen is the reserved, elegantly dressed married woman at the emotional center of Wong Kar-wai’s film "In the Mood for Love," whose restrained relationship with a neighbor explores longing, fidelity, and unspoken desire in 1960s Hong Kong.
E1907476 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: Su Li-zhen | Statement: [In the Mood for Love, mainCharacter, Su Li-zhen]
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: Su Li-zhen
Triple: [In the Mood for Love, mainCharacter, Su Li-zhen]
Generated description
Su Li-zhen is the reserved, elegantly dressed married woman at the emotional center of Wong Kar-wai’s film "In the Mood for Love," whose restrained relationship with a neighbor explores longing, fidelity, and unspoken desire in 1960s Hong Kong.

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_69f0bd48691081908cecad39bac591e0 completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66ccb2f0c8190afec245ff546681c completed May 2, 2026, 9:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a276ed222408190b6bbb1e9ab320cd7 completed June 9, 2026, 1:39 a.m.
NEDg Description generation batch_6a276f802e308190a92dfb272fc347d5 completed June 9, 2026, 1:42 a.m.
NED2 Entity disambiguation (via description) batch_6a277064150c8190a1d43e89ec3c4886 completed June 9, 2026, 1:46 a.m.
Created at: April 28, 2026, 5:04 p.m.