Triple
T2964249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Infanta Carlota Joaquina of Spain |
E80122
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Joaquina
Joaquina is the given name of Infanta Carlota Joaquina of Spain, a Spanish-born princess who became Queen consort of Portugal.
|
E314649
|
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: Joaquina | Statement: [Infanta Carlota Joaquina of Spain, givenName, Joaquina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joaquina Context triple: [Infanta Carlota Joaquina of Spain, givenName, Joaquina]
-
A.
Isabela
Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
-
B.
Carlota
Carlota is the feminine given name corresponding to Carlos, commonly used in Spanish- and Portuguese-speaking cultures.
-
C.
Consuelo
Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
-
D.
Lorena
Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
-
E.
Luisita
Luisita is a Spanish feminine given name, typically used as a diminutive or affectionate form of Luisa.
- 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: Joaquina Triple: [Infanta Carlota Joaquina of Spain, givenName, Joaquina]
Generated description
Joaquina is the given name of Infanta Carlota Joaquina of Spain, a Spanish-born princess who became Queen consort of Portugal.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joaquina Target entity description: Joaquina is the given name of Infanta Carlota Joaquina of Spain, a Spanish-born princess who became Queen consort of Portugal.
-
A.
Isabela
Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
-
B.
Carlota
Carlota is the feminine given name corresponding to Carlos, commonly used in Spanish- and Portuguese-speaking cultures.
-
C.
Consuelo
Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
-
D.
Lorena
Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
-
E.
Luisita
Luisita is a Spanish feminine given name, typically used as a diminutive or affectionate form of Luisa.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9958b1e48190a77f37bf63333c5b |
completed | March 8, 2026, 3:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc98d94481908282d21394dc24a7 |
completed | March 11, 2026, 5:24 a.m. |
| NEDg | Description generation | batch_69b0fd07b82881908d52ab2db2f2e54c |
completed | March 11, 2026, 5:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0fdba0fd88190a2e760f1770e846c |
completed | March 11, 2026, 5:29 a.m. |
Created at: March 8, 2026, 2:58 p.m.