Triple
T6400912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huasco Province |
E144058
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Freirina |
E145540
|
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: Freirina | Statement: [Huasco Province, hasSettlement, Freirina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Freirina Context triple: [Huasco Province, hasSettlement, Freirina]
-
A.
Freirina
chosen
Freirina is a small town and commune in northern Chile known for its agricultural activity and historic architecture within the Atacama Region.
-
B.
Arnalta
Arnalta is a comic nurse character in Claudio Monteverdi’s opera "L'incoronazione di Poppea," known for her earthy wisdom and humorous commentary.
-
C.
Frederuna
Frederuna was a 10th-century Frankish queen consort of West Francia as the first wife of King Charles the Simple.
-
D.
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
-
E.
Huelén
Huelén is the former indigenous name for Cerro Santa Lucía, a historic hill and urban park in central Santiago, Chile.
- 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_69c008dc56fc81908d43ffcc11d73bdd |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0689ae99881909ba427769fd317ae |
completed | March 22, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640bcc94c81909efb0253e8c0e7f5 |
completed | March 27, 2026, 8:33 a.m. |
Created at: March 22, 2026, 4:35 p.m.