Triple
T2964248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Infanta Carlota Joaquina of Spain |
E80122
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Carlota |
E293081
|
NE FINISHED |
How this triple was built (2 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: Carlota | Statement: [Infanta Carlota Joaquina of Spain, givenName, Carlota]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carlota Context triple: [Infanta Carlota Joaquina of Spain, givenName, Carlota]
-
A.
Carlota
chosen
Carlota is the feminine given name corresponding to Carlos, commonly used in Spanish- and Portuguese-speaking cultures.
-
B.
La Carlota
La Carlota is a component city in the province of Negros Occidental in the Philippines, known for its agricultural economy and historic sugar industry.
-
C.
Juana
Juana is the Spanish feminine given name equivalent to Jane, commonly used in Spanish-speaking cultures.
-
D.
Francisca
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
-
E.
María
"María" is a film featuring actress Taryn Power in a significant role.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69b108e14e288190bcca59b2d8132996 |
completed | March 11, 2026, 6:17 a.m. |
Created at: March 8, 2026, 2:58 p.m.