Triple
T8629755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saša |
E204369
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Sacha |
E204368
|
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: Sacha | Statement: [Saša, hasVariant, Sacha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sacha Context triple: [Saša, hasVariant, Sacha]
-
A.
Sacha
chosen
Sacha is a given name used in various cultures, often as a variant of Sasha and commonly serving as a diminutive of Alexander or Alexandra.
-
B.
Simao
Simao is the former name of Pu'er, a city in Yunnan Province, China, historically known for its tea trade.
-
C.
San Simon
San Simon is a municipality in the province of Pampanga in the Philippines, known for its agricultural economy and proximity to major urban centers in Central Luzon.
-
D.
Baquero
Baquero is a Spanish surname most notably associated with actress Ivana Baquero, known for her role in the film "Pan's Labyrinth."
-
E.
Satipo
Satipo is a town in central Peru that serves as the capital of Satipo Province in the Junín Region, known for its tropical climate and proximity to Amazonian forests.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc47406efc8190b559c68764b7455d |
completed | March 31, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebc0acf508190a090fb1edf9420d2 |
completed | April 2, 2026, 6:57 p.m. |
Created at: March 30, 2026, 6:27 p.m.