Triple
T2558261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | María Enríquez de Toledo y Guzmán |
E56779
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | María |
E65513
|
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: María | Statement: [María Enríquez de Toledo y Guzmán, givenName, María]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: María Context triple: [María Enríquez de Toledo y Guzmán, givenName, María]
-
A.
María
"María" is a film featuring actress Taryn Power in a significant role.
-
B.
María
chosen
María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
-
C.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
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.
Marta
Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
- 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_69ab4a4bfec081908039988ec4c86e28 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd33153fc8190aa106e23ee645f63 |
completed | March 7, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5d2061108190b6250d2943736ae4 |
completed | March 9, 2026, 11:52 p.m. |
Created at: March 6, 2026, 9:48 p.m.