Triple
T20916651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helena |
E515089
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Camargo |
—
|
NE NERFINISHED |
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: Camargo | Statement: [Helena, mainCharacter, Camargo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Camargo Context triple: [Helena, mainCharacter, Camargo]
-
A.
Camargo
chosen
Camargo is a Spanish-origin surname borne by numerous individuals and families across the Spanish- and Portuguese-speaking world.
-
B.
Camargo
Camargo is a municipality in the autonomous community of Cantabria in northern Spain, situated near the city of Santander.
-
C.
Camargo
Camargo is a small rural town located in Dewey County in western Oklahoma, United States.
-
D.
Osorio
Osorio is a Spanish-language surname borne by various notable individuals across sports, politics, and the arts.
-
E.
Barreto
Barreto is a Portuguese-origin surname common in Brazil and other Lusophone countries, borne by numerous notable figures in fields such as film, literature, and politics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4f9d5ec8190bb2bd27350ed341c |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6ec635f4881909a560fb891100d8c |
completed | April 21, 2026, 3:17 a.m. |
Created at: April 16, 2026, 12:48 p.m.