Triple
T3376682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Downey Sr. |
E71081
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Laura Ernst
Laura Ernst was the second wife of filmmaker Robert Downey Sr., known primarily for her connection to the Downey family.
|
E443548
|
NE FINISHED |
How this triple was built (4 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: Laura Ernst | Statement: [Robert Downey Sr., spouse, Laura Ernst]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Ernst Context triple: [Robert Downey Sr., spouse, Laura Ernst]
-
A.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
-
B.
Stefanie Ehrlich
Stefanie Ehrlich is known as a child of the prominent American biologist and author Paul Ehrlich.
-
C.
Elizabeth Kolb
Elizabeth Kolb was the woman who served as the ceremonial sponsor for the U.S. Navy battleship USS Pennsylvania (BB-38) at its launching.
-
D.
Laura Hastings-Smith
Laura Hastings-Smith is a British film and television producer known for working on acclaimed projects including the 2015 adaptation of Macbeth.
-
E.
Karen Rosenfelt
Karen Rosenfelt is an American film producer known for her work on major studio franchises and popular young adult adaptations, including entries in the Twilight series.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Laura Ernst Triple: [Robert Downey Sr., spouse, Laura Ernst]
Generated description
Laura Ernst was the second wife of filmmaker Robert Downey Sr., known primarily for her connection to the Downey family.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laura Ernst Target entity description: Laura Ernst was the second wife of filmmaker Robert Downey Sr., known primarily for her connection to the Downey family.
-
A.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
-
B.
Stefanie Ehrlich
Stefanie Ehrlich is known as a child of the prominent American biologist and author Paul Ehrlich.
-
C.
Elizabeth Kolb
Elizabeth Kolb was the woman who served as the ceremonial sponsor for the U.S. Navy battleship USS Pennsylvania (BB-38) at its launching.
-
D.
Laura Hastings-Smith
Laura Hastings-Smith is a British film and television producer known for working on acclaimed projects including the 2015 adaptation of Macbeth.
-
E.
Karen Rosenfelt
Karen Rosenfelt is an American film producer known for her work on major studio franchises and popular young adult adaptations, including entries in the Twilight series.
- F. None of above. chosen
Provenance (5 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_69ad85a7f80c8190a05e43013f298942 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2e776508190bc123fb17b36f062 |
completed | March 8, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b636ee2ed88190b37c7f6027d7623b |
completed | March 15, 2026, 4:34 a.m. |
| NEDg | Description generation | batch_69b6379d22448190976a84a18cf85ca6 |
completed | March 15, 2026, 4:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b638b105e88190a02c515a3416a026 |
completed | March 15, 2026, 4:42 a.m. |
Created at: March 8, 2026, 3:13 p.m.