Triple
T14247572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Fischli |
E353174
|
entity |
| Predicate | partnerInArtisticCollaboration |
P27398
|
FINISHED |
| Object | David Weiss |
E353717
|
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: David Weiss | Statement: [Peter Fischli, partnerInArtisticCollaboration, David Weiss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Weiss Context triple: [Peter Fischli, partnerInArtisticCollaboration, David Weiss]
-
A.
David Weiss
chosen
David Weiss is a common personal name shared by multiple notable individuals across fields such as law, music, and literature.
-
B.
David C. Weiss
David C. Weiss is an American attorney who serves as a U.S. Special Counsel and has been a key federal prosecutor in high-profile political and financial investigations.
-
C.
Jay Weiss
Jay Weiss is a New York real estate developer best known as the former husband of actress Kathleen Turner.
-
D.
Robert Weiss
Robert Weiss is a relatively common personal name shared by multiple individuals across various professions, including the arts, sciences, and public life.
-
E.
David N. Weiss
David N. Weiss is an American screenwriter best known for co-writing popular family and animated films such as "Shrek 2" and "The Smurfs."
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de629464f88190817b190731bab156 |
completed | April 14, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec86c6c6c8190957e398e3dcdd840 |
completed | May 9, 2026, 5:38 a.m. |
Created at: April 10, 2026, 1:08 a.m.