Triple
T17309287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linda Good |
E420248
|
entity |
| Predicate | hasSibling |
P363
|
FINISHED |
| Object |
Laura Good
Laura Good is the sibling of Linda Good, likely sharing a close familial and personal connection with her.
|
E1261927
|
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 Good | Statement: [Linda Good, hasSibling, Laura Good]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Good Context triple: [Linda Good, hasSibling, Laura Good]
-
A.
Laura Leslie Dick
Laura Leslie Dick is a member of the Dick family, known primarily as the sister of television producer and Philip K. Dick estate executor Isa Dick Hackett.
-
B.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
C.
Kimberly Caldwell
Kimberly Caldwell is an American singer, television host, and actress who gained national recognition as a standout contestant on the second season of American Idol.
-
D.
Laura Eastman
Laura Eastman is a member of the Eastman family, known for its connections to prominent figures in the music and entertainment industry.
-
E.
Laura Harrington
Laura Harrington is an American actress best known for her role in the 1986 Stephen King film "Maximum Overdrive."
- 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 Good Triple: [Linda Good, hasSibling, Laura Good]
Generated description
Laura Good is the sibling of Linda Good, likely sharing a close familial and personal connection with her.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laura Good Target entity description: Laura Good is the sibling of Linda Good, likely sharing a close familial and personal connection with her.
-
A.
Laura Leslie Dick
Laura Leslie Dick is a member of the Dick family, known primarily as the sister of television producer and Philip K. Dick estate executor Isa Dick Hackett.
-
B.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
C.
Kimberly Caldwell
Kimberly Caldwell is an American singer, television host, and actress who gained national recognition as a standout contestant on the second season of American Idol.
-
D.
Laura Eastman
Laura Eastman is a member of the Eastman family, known for its connections to prominent figures in the music and entertainment industry.
-
E.
Laura Harrington
Laura Harrington is an American actress best known for her role in the 1986 Stephen King film "Maximum Overdrive."
- 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439970cf08190bc9e49ba830da0d9 |
completed | April 19, 2026, 2:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180e30934819087b7c838c8874aff |
completed | May 11, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_6a0185a7e5188190a15d835019fc226f |
completed | May 11, 2026, 7:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0186504460819097f80978b03c7296 |
completed | May 11, 2026, 7:33 a.m. |
Created at: April 10, 2026, 5:43 a.m.