Triple

T12412167
Position Surface form Disambiguated ID Type / Status
Subject Jean Tigana E296543 entity
Predicate familyName P18 FINISHED
Object Tigana
Tigana is a French former professional footballer and manager, best known as a dynamic midfielder for clubs like Bordeaux and the French national team during the 1980s.
 E985436 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: Tigana | Statement: [Jean Tigana, familyName, Tigana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tigana
Context triple: [Jean Tigana, familyName, Tigana]
  • A. Tigani
    Tigani is the former name of the town now known as Pythagoreio, a historic coastal settlement on the Greek island of Samos.
  • B. Taliska
    Taliska is a Mannish language of Middle-earth in J.R.R. Tolkien’s legendarium, spoken by the early Men of Beleriand.
  • C. Teurnia
    Teurnia was an important ancient Roman city that served as a major administrative and cultural center in the province of Noricum, located in what is now southern Austria.
  • D. Tialo
    Tialo is an Austronesian language of the Tomini–Tolitoli subgroup spoken in Central Sulawesi, Indonesia.
  • E. Támara
    Támara is a small rural municipality in eastern Colombia known for its agricultural economy and location in the Andean foothills of the Casanare region.
  • 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: Tigana
Triple: [Jean Tigana, familyName, Tigana]
Generated description
Tigana is a French former professional footballer and manager, best known as a dynamic midfielder for clubs like Bordeaux and the French national team during the 1980s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tigana
Target entity description: Tigana is a French former professional footballer and manager, best known as a dynamic midfielder for clubs like Bordeaux and the French national team during the 1980s.
  • A. Tigani
    Tigani is the former name of the town now known as Pythagoreio, a historic coastal settlement on the Greek island of Samos.
  • B. Taliska
    Taliska is a Mannish language of Middle-earth in J.R.R. Tolkien’s legendarium, spoken by the early Men of Beleriand.
  • C. Teurnia
    Teurnia was an important ancient Roman city that served as a major administrative and cultural center in the province of Noricum, located in what is now southern Austria.
  • D. Tialo
    Tialo is an Austronesian language of the Tomini–Tolitoli subgroup spoken in Central Sulawesi, Indonesia.
  • E. Támara
    Támara is a small rural municipality in eastern Colombia known for its agricultural economy and location in the Andean foothills of the Casanare region.
  • 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_69d6ad9f464c81909db36d7e96e34b9e completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d6b0f9c8190813b6fe3f97570ac completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63efe60388190944fe3226be4cc7c completed May 2, 2026, 6:14 p.m.
NEDg Description generation batch_69f64305712881908a8ccd884b19e602 completed May 2, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69f644dc87b08190a99846fee86b0570 completed May 2, 2026, 6:39 p.m.
Created at: April 8, 2026, 9:55 p.m.