Triple
T10815440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heunisch Weiss |
E255216
|
entity |
| Predicate | parentOf |
P120
|
FINISHED |
| Object | Sacy |
E223448
|
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: Sacy | Statement: [Heunisch Weiss, parentOf, Sacy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sacy Context triple: [Heunisch Weiss, parentOf, Sacy]
-
A.
Sacy
chosen
Sacy is a white grape variety from central France, historically used in light, crisp wines and blends, particularly in regions like Burgundy.
-
B.
Merlav
Merlav is a small island and Oceanic language community in Vanuatu, known for its distinct Mwerlap language and culture.
-
C.
Claverie
Claverie is a French-origin surname borne by various notable individuals, including figures in the arts, sciences, and public life.
-
D.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
E.
Souvestre
Souvestre is a French surname notably borne by educator Marie Souvestre, known for her progressive influence on women’s education in the 19th century.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733edab248190b2cf7f7bc2684468 |
completed | April 9, 2026, 5:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de8546b41081909e13152c4df2eb1c |
completed | April 14, 2026, 6:19 p.m. |
Created at: April 8, 2026, 9:18 p.m.