Triple

T379707
Position Surface form Disambiguated ID Type / Status
Subject Casablanca E8651 entity
Predicate character P662 FINISHED
Object Captain Louis Renault
Captain Louis Renault is the charmingly corrupt yet ultimately principled French police prefect in the classic 1942 film "Casablanca."
E48308 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: Captain Louis Renault | Statement: [Casablanca, character, Captain Louis Renault]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Captain Louis Renault
Context triple: [Casablanca, character, Captain Louis Renault]
  • A. Hector Lefuel
    Hector Lefuel was a 19th-century French architect best known for his major role in completing and expanding the Louvre under Napoleon III, helping define the Second Empire architectural style.
  • B. Stephen Sauvestre
    Stephen Sauvestre was a French architect best known for designing the architectural embellishments and final aesthetic of the Eiffel Tower.
  • C. Aimable Pélissier
    Aimable Pélissier was a 19th-century French marshal and military commander best known for his leadership in key campaigns of the Crimean War.
  • D. Armand
    Armand is the given first name of the French poet and Nobel laureate Sully Prudhomme.
  • E. Jacques
    Jacques is the French form of the given name James, commonly used in French-speaking countries.
  • 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: Captain Louis Renault
Triple: [Casablanca, character, Captain Louis Renault]
Generated description
Captain Louis Renault is the charmingly corrupt yet ultimately principled French police prefect in the classic 1942 film "Casablanca."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Captain Louis Renault
Target entity description: Captain Louis Renault is the charmingly corrupt yet ultimately principled French police prefect in the classic 1942 film "Casablanca."
  • A. Hector Lefuel
    Hector Lefuel was a 19th-century French architect best known for his major role in completing and expanding the Louvre under Napoleon III, helping define the Second Empire architectural style.
  • B. Stephen Sauvestre
    Stephen Sauvestre was a French architect best known for designing the architectural embellishments and final aesthetic of the Eiffel Tower.
  • C. Aimable Pélissier
    Aimable Pélissier was a 19th-century French marshal and military commander best known for his leadership in key campaigns of the Crimean War.
  • D. Armand
    Armand is the given first name of the French poet and Nobel laureate Sully Prudhomme.
  • E. Jacques
    Jacques is the French form of the given name James, commonly used in French-speaking countries.
  • 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec2b07248190979229bad3a741c9 completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3fafe091881908fdf8ddbb6b8a7e6 completed March 1, 2026, 8:38 a.m.
NEDg Description generation batch_69a3fba8a31881909a32dca83c07e197 completed March 1, 2026, 8:41 a.m.
NED2 Entity disambiguation (via description) batch_69a3fc96cbd88190b05e70c73cbb45c0 completed March 1, 2026, 8:45 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.