Triple
T21408797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melipilla Province |
E528111
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | María Pinto |
E164497
|
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 Pinto | Statement: [Melipilla Province, hasMunicipality, María Pinto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: María Pinto Context triple: [Melipilla Province, hasMunicipality, María Pinto]
-
A.
María Pinto
chosen
María Pinto is a rural commune and town in central Chile known for its agricultural activities and location within the Santiago Metropolitan Region.
-
B.
María Carrasco
María Carrasco is a Spanish flamenco-pop singer known for her emotive vocal style and early success as a child artist.
-
C.
María Portillo
María Portillo is a notable individual recognized for achievements significant enough to be associated with the surname Portillo.
-
D.
María Barranco
María Barranco is a Spanish actress best known for her work in Pedro Almodóvar’s films and for her acclaimed performances in late-20th-century Spanish cinema.
-
E.
María Valenzuela
María Valenzuela is an Argentine actress known for her extensive work in television, film, and theater across several decades.
- 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_69e0b520ee3c8190abddbee7e37e834c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b1b316a48190ad43394dc35a54e0 |
completed | April 22, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09e125fe788190a53cffac7da540e2 |
completed | May 17, 2026, 3:39 p.m. |
Created at: April 16, 2026, 5:33 p.m.