Triple
T9414380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rudy Law |
E226977
|
entity |
| Predicate | sibling |
P363
|
FINISHED |
| Object | Ada Law |
E171648
|
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: Ada Law | Statement: [Rudy Law, sibling, Ada Law]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ada Law Context triple: [Rudy Law, sibling, Ada Law]
-
A.
Ada Law
chosen
Ada Law is one of the children of English actor Jude Law.
-
B.
Kathryn Bostic
Kathryn Bostic is an American composer and pianist known for her evocative film and theater scores, particularly in independent cinema and stage productions.
-
C.
Esther Dyson
Esther Dyson is a prominent technology investor, journalist, and philanthropist known for her early involvement in the digital economy and advocacy on issues such as health, space, and technology policy.
-
D.
Susan B. Landau
Susan B. Landau is a film producer best known for her work on the popular 1993 sports comedy "Cool Runnings."
-
E.
Susan Norton
Susan Norton is a central protagonist in Stephen King’s horror novel "Salem’s Lot," known for her involvement in uncovering and confronting the vampire infestation in the town.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd68c7bd648190b17f082883c98239 |
completed | April 1, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d107b63cf48190a072e3434a7b85a8 |
completed | April 4, 2026, 12:44 p.m. |
Created at: March 30, 2026, 7:47 p.m.