Triple

T17217740
Position Surface form Disambiguated ID Type / Status
Subject Sophie Marceau E417894 entity
Predicate notableWork P4 FINISHED
Object Fanfan
Fanfan is a romantic film best known for starring French actress Sophie Marceau.
 E1258353 NE FINISHED

How this triple was built (4 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: Fanfan | Statement: [Sophie Marceau, notableWork, Fanfan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fanfan
Context triple: [Sophie Marceau, notableWork, Fanfan]
  • A. FAN
    FAN is the French acronym for Niger's national military, responsible for the country's defense and security operations.
  • B. FAN
    FAN was a key rebel armed group in Chad that played a major role in the country’s internal conflicts during the late 20th century.
  • C. Fan Hir
    Fan Hir is a long, narrow ridge and mountain in the Brecon Beacons of South Wales, popular with hikers for its dramatic escarpments and expansive views.
  • D. Fan
    Fan is a common Chinese surname borne by numerous historical and contemporary figures across politics, arts, sports, and other fields.
  • E. Fan of a Fan
    Fan of a Fan is a collaborative hip hop mixtape by Tyga and Chris Brown that helped boost both artists' profiles with its club-ready tracks and catchy hooks.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Fanfan
Triple: [Sophie Marceau, notableWork, Fanfan]
Generated description
Fanfan is a romantic film best known for starring French actress Sophie Marceau.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fanfan
Target entity description: Fanfan is a romantic film best known for starring French actress Sophie Marceau.
  • A. FAN
    FAN is the French acronym for Niger's national military, responsible for the country's defense and security operations.
  • B. FAN
    FAN was a key rebel armed group in Chad that played a major role in the country’s internal conflicts during the late 20th century.
  • C. Fan Hir
    Fan Hir is a long, narrow ridge and mountain in the Brecon Beacons of South Wales, popular with hikers for its dramatic escarpments and expansive views.
  • D. Fan
    Fan is a common Chinese surname borne by numerous historical and contemporary figures across politics, arts, sports, and other fields.
  • E. Fan of a Fan
    Fan of a Fan is a collaborative hip hop mixtape by Tyga and Chris Brown that helped boost both artists' profiles with its club-ready tracks and catchy hooks.
  • F. None of above. chosen

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_69d886d779488190b131369541c04e7d completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42ddb2b148190b3b50572cc285e3d completed April 19, 2026, 1:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01675381a0819094ed04eac636440b completed May 11, 2026, 5:21 a.m.
NEDg Description generation batch_6a016b8609dc8190bfd3e1b6ff715d65 completed May 11, 2026, 5:39 a.m.
NED2 Entity disambiguation (via description) batch_6a016c5018e48190974c124c3433bcc6 completed May 11, 2026, 5:42 a.m.
Created at: April 10, 2026, 5:38 a.m.