Triple
T2467183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of São Paulo |
E55278
|
entity |
| Predicate | hasCampus |
P116
|
FINISHED |
| Object |
Lorena
Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
|
E270035
|
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: Lorena | Statement: [University of São Paulo, hasCampus, Lorena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lorena Context triple: [University of São Paulo, hasCampus, Lorena]
-
A.
Consuelo
Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
-
B.
Camile Velasco
Camile Velasco is a Filipino-American singer who gained national recognition as a finalist on the third season of the television talent show American Idol.
-
C.
Elena Alvarez
Elena Alvarez is a socially conscious, feminist teenage daughter in the Cuban-American family at the heart of the sitcom "One Day at a Time" (2017).
-
D.
Leona Vicario
Leona Vicario was a prominent Mexican independence heroine, journalist, and supporter of the insurgent cause against Spanish rule in the early 19th century.
-
E.
Jacqueline
Jacqueline is a feminine given name most famously borne by former U.S. First Lady Jacqueline Kennedy Onassis.
- 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: Lorena Triple: [University of São Paulo, hasCampus, Lorena]
Generated description
Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lorena Target entity description: Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
-
A.
Consuelo
Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
-
B.
Camile Velasco
Camile Velasco is a Filipino-American singer who gained national recognition as a finalist on the third season of the television talent show American Idol.
-
C.
Elena Alvarez
Elena Alvarez is a socially conscious, feminist teenage daughter in the Cuban-American family at the heart of the sitcom "One Day at a Time" (2017).
-
D.
Leona Vicario
Leona Vicario was a prominent Mexican independence heroine, journalist, and supporter of the insurgent cause against Spanish rule in the early 19th century.
-
E.
Jacqueline
Jacqueline is a feminine given name most famously borne by former U.S. First Lady Jacqueline Kennedy Onassis.
- 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd13310a8819095fd70672f933aa3 |
completed | March 7, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af179f90e881909c09edb961b13a75 |
completed | March 9, 2026, 6:55 p.m. |
| NEDg | Description generation | batch_69af195ec8788190ae2f94f7cd86e605 |
completed | March 9, 2026, 7:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af1a28591c8190ab4f3dca260766f5 |
completed | March 9, 2026, 7:06 p.m. |
Created at: March 6, 2026, 9:44 p.m.