Triple
T5708933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Farrow |
E125855
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | María |
E542342
|
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: [Mia Farrow, givenName, María]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: María Context triple: [Mia Farrow, givenName, María]
-
A.
María
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.
-
B.
María
"María" is a film featuring actress Taryn Power in a significant role.
-
C.
María
chosen
María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
-
D.
María
María is the given first name of Josefa Ortiz de Domínguez, a prominent figure in Mexico’s War of Independence.
-
E.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
- 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_69c0082d6fe48190b777fb383769e5c8 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0248ab6a88190be17bdc32c36e5cb |
completed | March 22, 2026, 5:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a159077081909114b09f16ad2a1a |
completed | March 23, 2026, 2:11 a.m. |
Created at: March 22, 2026, 3:46 p.m.