Triple

T3407852
Position Surface form Disambiguated ID Type / Status
Subject Ivana Baquero E71817 entity
Predicate notableWork P4 FINISHED
Object Gelo
Gelo is a film featuring Spanish actress Ivana Baquero in a prominent role.
 E355301 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: Gelo | Statement: [Ivana Baquero, notableWork, Gelo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gelo
Context triple: [Ivana Baquero, notableWork, Gelo]
  • A. Golus
    Golus is a Yiddish term referring to the Jewish exile and dispersion from their ancestral homeland, encompassing both the physical diaspora and its spiritual-historical implications.
  • B. Gugino
    Gugino is the surname of American actress Carla Gugino, known for her versatile roles in film and television.
  • C. Giogha
    Giogha is the Scottish Gaelic name for the small Hebridean island of Gigha off the west coast of Kintyre in Scotland.
  • D. Ozem
    Ozem is a biblical figure mentioned in the Old Testament as one of Jesse’s sons and thus a brother of King David.
  • E. Giporlos
    Giporlos is a coastal municipality in the province of Eastern Samar in the Philippines, known for its fishing communities and scenic seaside landscapes.
  • 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: Gelo
Triple: [Ivana Baquero, notableWork, Gelo]
Generated description
Gelo is a film featuring Spanish actress Ivana Baquero in a prominent role.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gelo
Target entity description: Gelo is a film featuring Spanish actress Ivana Baquero in a prominent role.
  • A. Golus
    Golus is a Yiddish term referring to the Jewish exile and dispersion from their ancestral homeland, encompassing both the physical diaspora and its spiritual-historical implications.
  • B. Gugino
    Gugino is the surname of American actress Carla Gugino, known for her versatile roles in film and television.
  • C. Giogha
    Giogha is the Scottish Gaelic name for the small Hebridean island of Gigha off the west coast of Kintyre in Scotland.
  • D. Ozem
    Ozem is a biblical figure mentioned in the Old Testament as one of Jesse’s sons and thus a brother of King David.
  • E. Giporlos
    Giporlos is a coastal municipality in the province of Eastern Samar in the Philippines, known for its fishing communities and scenic seaside landscapes.
  • 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_69ad85ac312481909e7027ced1456a9f completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb8ede9c48190b13b0f5e7474e7fa completed March 8, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bdaf06c8190a8102a4e3c728066 completed March 12, 2026, 11:27 p.m.
NEDg Description generation batch_69b34e486c3c81908e73c5b75baf119c completed March 12, 2026, 11:37 p.m.
NED2 Entity disambiguation (via description) batch_69b34fc420b08190baee678721b1b32c completed March 12, 2026, 11:44 p.m.
Created at: March 8, 2026, 3:15 p.m.