Triple
T20173033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miquela |
E492016
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object |
Micaela
Micaela is a given name commonly used in Spanish- and Portuguese-speaking countries, often considered a variant of Michaela.
|
E915702
|
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: Micaela | Statement: [Miquela, relatedName, Micaela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Micaela Context triple: [Miquela, relatedName, Micaela]
-
A.
Mikaela
Mikaela is a feminine given name most prominently associated with American alpine ski champion Mikaela Shiffrin.
-
B.
Eliana
Eliana is a feminine given name of Hebrew and Latin origin, often interpreted to mean "God has answered" or "my God has answered."
-
C.
Noelia
Noelia is a feminine given name, commonly used in Spanish-speaking countries and derived from the name Noel.
-
D.
Penelope Alvarez
Penelope Alvarez is the Cuban-American single mother and military veteran at the heart of the 2017 reboot of "One Day at a Time," navigating family life, mental health, and cultural identity with humor and resilience.
-
E.
Mónica
Mónica is the given name of Spanish singer and songwriter Mónica Naranjo, known for her powerful voice and dramatic pop music style.
- 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: Micaela Triple: [Miquela, relatedName, Micaela]
Generated description
Micaela is a given name commonly used in Spanish- and Portuguese-speaking countries, often considered a variant of Michaela.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Micaela Target entity description: Micaela is a given name commonly used in Spanish- and Portuguese-speaking countries, often considered a variant of Michaela.
-
A.
Mikaela
chosen
Mikaela is a feminine given name most prominently associated with American alpine ski champion Mikaela Shiffrin.
-
B.
Eliana
Eliana is a feminine given name of Hebrew and Latin origin, often interpreted to mean "God has answered" or "my God has answered."
-
C.
Noelia
Noelia is a feminine given name, commonly used in Spanish-speaking countries and derived from the name Noel.
-
D.
Penelope Alvarez
Penelope Alvarez is the Cuban-American single mother and military veteran at the heart of the 2017 reboot of "One Day at a Time," navigating family life, mental health, and cultural identity with humor and resilience.
-
E.
Mónica
Mónica is the given name of Spanish singer and songwriter Mónica Naranjo, known for her powerful voice and dramatic pop music style.
- F. None of above.
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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66849709c81909b65b421282f9f3b |
completed | April 20, 2026, 5:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a083484eaa4819083462003ba5a78b0 |
completed | May 16, 2026, 9:10 a.m. |
| NEDg | Description generation | batch_6a083851ccf081909de87d5070e969f9 |
completed | May 16, 2026, 9:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0838c3694c819097516c8de60864fb |
completed | May 16, 2026, 9:28 a.m. |
Created at: April 11, 2026, 11:35 p.m.