Triple
T16292116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Lassally |
E395550
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lassally |
E953765
|
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: Lassally | Statement: [Peter Lassally, familyName, Lassally]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lassally Context triple: [Peter Lassally, familyName, Lassally]
-
A.
Lassally
chosen
Lassally is a German-origin surname most notably associated with Walter Lassally, an acclaimed cinematographer known for his work in British and Greek cinema.
-
B.
Lardé
Lardé is the surname of Alicia Esther Lardé, a Salvadoran-born physicist and the first wife of mathematician John Nash.
-
C.
Lusser
Lusser is a German surname most notably associated with engineer Robert Lusser, known for his contributions to aeronautics and reliability engineering.
-
D.
Jacquère
Jacquère is a light, crisp white wine grape variety primarily grown in the Alpine regions of eastern France, known for producing fresh, mineral-driven wines.
-
E.
Louison
Louison is the naive, good-hearted handyman protagonist in the darkly comic French film "Delicatessen."
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24919345881909ba4e7fe2e59340f |
completed | April 17, 2026, 2:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f97895081909f22ded3507afe14 |
completed | May 10, 2026, 6:03 a.m. |
Created at: April 10, 2026, 5:05 a.m.