Triple
T5009852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pedro Santana Lopes |
E112589
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Santana Lopes
Santana Lopes is a Portuguese lawyer and politician who briefly served as Prime Minister of Portugal in 2004–2005.
|
E486041
|
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: Santana Lopes | Statement: [Pedro Santana Lopes, familyName, Santana Lopes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santana Lopes Context triple: [Pedro Santana Lopes, familyName, Santana Lopes]
-
A.
Eliane Cavalleiro
Eliane Cavalleiro is a Brazilian academic and activist known for her work on racial and gender equality in education.
-
B.
Manuela Veloso
Manuela Veloso is a prominent computer scientist and roboticist known for her pioneering work in artificial intelligence and multi-agent robotics.
-
C.
Fernanda Tadeu
Fernanda Tadeu is a Portuguese educator and public figure best known as the wife of former Prime Minister António Costa.
-
D.
Sara Sampaio
Sara Sampaio is a Portuguese fashion model best known for her work with Victoria’s Secret and appearances in major international fashion magazines and campaigns.
-
E.
Lais Ribeiro
Lais Ribeiro is a Brazilian fashion model best known for her work with Victoria’s Secret and appearances in its high-profile runway shows.
- 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: Santana Lopes Triple: [Pedro Santana Lopes, familyName, Santana Lopes]
Generated description
Santana Lopes is a Portuguese lawyer and politician who briefly served as Prime Minister of Portugal in 2004–2005.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Santana Lopes Target entity description: Santana Lopes is a Portuguese lawyer and politician who briefly served as Prime Minister of Portugal in 2004–2005.
-
A.
Eliane Cavalleiro
Eliane Cavalleiro is a Brazilian academic and activist known for her work on racial and gender equality in education.
-
B.
Manuela Veloso
Manuela Veloso is a prominent computer scientist and roboticist known for her pioneering work in artificial intelligence and multi-agent robotics.
-
C.
Fernanda Tadeu
Fernanda Tadeu is a Portuguese educator and public figure best known as the wife of former Prime Minister António Costa.
-
D.
Sara Sampaio
Sara Sampaio is a Portuguese fashion model best known for her work with Victoria’s Secret and appearances in major international fashion magazines and campaigns.
-
E.
Lais Ribeiro
Lais Ribeiro is a Brazilian fashion model best known for her work with Victoria’s Secret and appearances in its high-profile runway shows.
- 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_69bd4434acb8819086679dbeccc2fe54 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd730bdb208190bebd7f22839ab6e5 |
completed | March 20, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9269e72881908ea49a77a83b8958 |
completed | March 21, 2026, 12:43 p.m. |
| NEDg | Description generation | batch_69be933f3ed08190a128c1b3c9b3b1c3 |
completed | March 21, 2026, 12:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be940b2eac819099001d403501afac |
completed | March 21, 2026, 12:50 p.m. |
Created at: March 20, 2026, 1:35 p.m.