Triple

T2068945
Position Surface form Disambiguated ID Type / Status
Subject Provisional Government of the French Republic E45970 entity
Predicate shortName P43 FINISHED
Object GPRF
GPRF is the commonly used abbreviation for the Provisional Government of the French Republic, which governed France immediately after its liberation in World War II.
 E230892 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: GPRF | Statement: [Provisional Government of the French Republic, shortName, GPRF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GPRF
Context triple: [Provisional Government of the French Republic, shortName, GPRF]
  • A. GRPM
    GRPM is a regional museum in Grand Rapids, Michigan, known for its exhibits on local history, science, and culture.
  • B. GROM
    GROM is Poland’s elite special operations unit renowned for high-risk counterterrorism, hostage rescue, and unconventional warfare missions.
  • C. GR-F
    GR-F is the ISO 3166-2 regional code assigned to the Greek administrative region of Thessaly.
  • D. GUF
    GUF is the three-letter ISO 3166-1 alpha-3 country code assigned to French Guiana.
  • E. GP
    GP is the vehicle registration code used on license plates for the German town and district of Göppingen in the state of Baden-Württemberg.
  • 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: GPRF
Triple: [Provisional Government of the French Republic, shortName, GPRF]
Generated description
GPRF is the commonly used abbreviation for the Provisional Government of the French Republic, which governed France immediately after its liberation in World War II.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GPRF
Target entity description: GPRF is the commonly used abbreviation for the Provisional Government of the French Republic, which governed France immediately after its liberation in World War II.
  • A. GRPM
    GRPM is a regional museum in Grand Rapids, Michigan, known for its exhibits on local history, science, and culture.
  • B. GROM
    GROM is Poland’s elite special operations unit renowned for high-risk counterterrorism, hostage rescue, and unconventional warfare missions.
  • C. GR-F
    GR-F is the ISO 3166-2 regional code assigned to the Greek administrative region of Thessaly.
  • D. GUF
    GUF is the three-letter ISO 3166-1 alpha-3 country code assigned to French Guiana.
  • E. GP
    GP is the vehicle registration code used on license plates for the German town and district of Göppingen in the state of Baden-Württemberg.
  • 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_69a8891b38288190abd572ccad9b6928 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9f51a008190aead0173a9289204 completed March 7, 2026, 5:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae27289eb081909bfbc9bf2cd14878 completed March 9, 2026, 1:49 a.m.
NEDg Description generation batch_69ae28c163088190818891302f7faa8e completed March 9, 2026, 1:56 a.m.
NED2 Entity disambiguation (via description) batch_69ae292a9d7481909acbc3a5f24ff0b9 completed March 9, 2026, 1:58 a.m.
Created at: March 4, 2026, 7:41 p.m.