Triple
T12173389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Clara Broncos women's soccer |
E290028
|
entity |
| Predicate | notableAlumna |
P4387
|
FINISHED |
| Object |
Sofia Huerta
Sofia Huerta is a professional American soccer player and United States women’s national team defender known for her versatility and attacking contributions from the back line.
|
E1000332
|
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: Sofia Huerta | Statement: [Santa Clara Broncos women's soccer, notableAlumna, Sofia Huerta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sofia Huerta Context triple: [Santa Clara Broncos women's soccer, notableAlumna, Sofia Huerta]
-
A.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
B.
Elena Flores
Elena Flores is a Spanish politician who succeeded Marta Fernández Miranda in public office.
-
C.
Marisa Ramirez
Marisa Ramirez is an American actress best known for her role as Detective Maria Baez on the television series "Blue Bloods."
-
D.
Yahira Garcia
Yahira Garcia is an actress known for her role in the 2012 police drama film "End of Watch."
-
E.
Angelica Fuentes
Angelica Fuentes is a Mexican businesswoman and philanthropist known for her leadership roles in the energy sector and in professional soccer, as well as her advocacy for women's empowerment in Latin America.
- 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: Sofia Huerta Triple: [Santa Clara Broncos women's soccer, notableAlumna, Sofia Huerta]
Generated description
Sofia Huerta is a professional American soccer player and United States women’s national team defender known for her versatility and attacking contributions from the back line.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sofia Huerta Target entity description: Sofia Huerta is a professional American soccer player and United States women’s national team defender known for her versatility and attacking contributions from the back line.
-
A.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
B.
Elena Flores
Elena Flores is a Spanish politician who succeeded Marta Fernández Miranda in public office.
-
C.
Marisa Ramirez
Marisa Ramirez is an American actress best known for her role as Detective Maria Baez on the television series "Blue Bloods."
-
D.
Yahira Garcia
Yahira Garcia is an actress known for her role in the 2012 police drama film "End of Watch."
-
E.
Angelica Fuentes
Angelica Fuentes is a Mexican businesswoman and philanthropist known for her leadership roles in the energy sector and in professional soccer, as well as her advocacy for women's empowerment in Latin America.
- 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915dab42881908e2580c631d4d1cf |
completed | April 10, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c60a7d081908e402c15b7e38596 |
completed | May 2, 2026, 10:36 p.m. |
| NEDg | Description generation | batch_69f67db4dd2081909a238e368645e899 |
completed | May 2, 2026, 10:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f67ececce8819080335e67bd747057 |
completed | May 2, 2026, 10:46 p.m. |
Created at: April 8, 2026, 9:50 p.m.