Triple

T6614313
Position Surface form Disambiguated ID Type / Status
Subject Nani E149309 entity
Predicate youthClub P1088 FINISHED
Object Real Massamá
Real Massamá is a Portuguese football club known for its youth academy that helped develop players such as winger Nani.
E601027 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: Real Massamá | Statement: [Nani, youthClub, Real Massamá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Real Massamá
Context triple: [Nani, youthClub, Real Massamá]
  • A. Rossio
    Rossio is a central Lisbon square and major transport hub known for its historic architecture, lively atmosphere, and role as a key meeting point in the city.
  • B. Massa
    Massa is a small coastal town in southern Morocco known for its proximity to the Souss-Massa National Park and its traditional Berber culture.
  • C. Massa
    Massa is a historic city in northwestern Tuscany, Italy, known for its marble industry and proximity to the Apuan Alps and the Tyrrhenian coast.
  • D. Massa
    Massa is a biblical figure listed among the descendants of Ishmael in the Hebrew Bible.
  • E. Masa
    Masa is a Central Chadic language spoken primarily in parts of Cameroon and Chad.
  • 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: Real Massamá
Triple: [Nani, youthClub, Real Massamá]
Generated description
Real Massamá is a Portuguese football club known for its youth academy that helped develop players such as winger Nani.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Real Massamá
Target entity description: Real Massamá is a Portuguese football club known for its youth academy that helped develop players such as winger Nani.
  • A. Rossio
    Rossio is a central Lisbon square and major transport hub known for its historic architecture, lively atmosphere, and role as a key meeting point in the city.
  • B. Massa
    Massa is a small coastal town in southern Morocco known for its proximity to the Souss-Massa National Park and its traditional Berber culture.
  • C. Massa
    Massa is a historic city in northwestern Tuscany, Italy, known for its marble industry and proximity to the Apuan Alps and the Tyrrhenian coast.
  • D. Massa
    Massa is a biblical figure listed among the descendants of Ishmael in the Hebrew Bible.
  • E. Masa
    Masa is a Central Chadic language spoken primarily in parts of Cameroon and Chad.
  • 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_69c687ebc680819094caf71faba2efe2 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af569ecc8190a9526decc745f0a0 completed March 27, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cbd651c08190a19513645fc781c9 completed March 27, 2026, 6:26 p.m.
NEDg Description generation batch_69c6cd89b51c81909ea17d391732630e completed March 27, 2026, 6:33 p.m.
NED2 Entity disambiguation (via description) batch_69c6ce70442c8190a12a6c6eb76c5269 completed March 27, 2026, 6:37 p.m.
Created at: March 27, 2026, 1:57 p.m.