Triple

T24428296
Position Surface form Disambiguated ID Type / Status
Subject A Taxi Driver E615921 entity
Predicate character P662 FINISHED
Object Kim Man‑seob
Kim Man-seob is the fictional Seoul taxi driver protagonist of the film "A Taxi Driver," who becomes unexpectedly involved in the historic 1980 Gwangju Uprising.
 E2281922 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: Kim Man‑seob | Statement: [A Taxi Driver, character, Kim Man‑seob]
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: Kim Man‑seob
Triple: [A Taxi Driver, character, Kim Man‑seob]
Generated description
Kim Man-seob is the fictional Seoul taxi driver protagonist of the film "A Taxi Driver," who becomes unexpectedly involved in the historic 1980 Gwangju Uprising.

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_69e2d7eadb248190a867130fe45f0388 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f296a983d88190904b559694363a19 completed April 29, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a420dee9df881908a27a76b371b27dd completed June 29, 2026, 6:17 a.m.
NEDg Description generation batch_6a420e962d948190ae48e6e87e19a82a completed June 29, 2026, 6:20 a.m.
NED2 Entity disambiguation (via description) batch_6a420f4106fc819089d72df446df93a2 completed June 29, 2026, 6:22 a.m.
Created at: April 18, 2026, 2:15 a.m.