Triple
T10225756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucy Harmon |
E243198
|
entity |
| Predicate | hasMother |
P1909
|
FINISHED |
| Object |
Sara Harmon
Sara Harmon is the mother of Lucy Harmon.
|
E865256
|
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: Sara Harmon | Statement: [Lucy Harmon, hasMother, Sara Harmon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sara Harmon Context triple: [Lucy Harmon, hasMother, Sara Harmon]
-
A.
Sara Haden
Sara Haden was an American character actress best known for her supporting roles in classic Hollywood films of the 1930s and 1940s, including several entries in the Andy Hardy series.
-
B.
Milynn Sarley
Milynn Sarley is an American actress and internet personality known for her roles in low-budget fantasy and action films as well as her presence in online geek and gaming communities.
-
C.
Sara Allgood
Sara Allgood was an Irish stage and film actress known for her character roles in early 20th-century theatre and classic Hollywood cinema.
-
D.
Sara Braun
Sara Braun was a prominent late 19th- and early 20th-century businesswoman and philanthropist in Chilean Patagonia, known for her influential role in regional development and society.
-
E.
Sara Henry
Sara Henry is known as the wife of American voice actor and comedian Mike Henry, recognized for his work on shows like Family Guy.
- 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: Sara Harmon Triple: [Lucy Harmon, hasMother, Sara Harmon]
Generated description
Sara Harmon is the mother of Lucy Harmon.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sara Harmon Target entity description: Sara Harmon is the mother of Lucy Harmon.
-
A.
Sara Haden
Sara Haden was an American character actress best known for her supporting roles in classic Hollywood films of the 1930s and 1940s, including several entries in the Andy Hardy series.
-
B.
Milynn Sarley
Milynn Sarley is an American actress and internet personality known for her roles in low-budget fantasy and action films as well as her presence in online geek and gaming communities.
-
C.
Sara Allgood
Sara Allgood was an Irish stage and film actress known for her character roles in early 20th-century theatre and classic Hollywood cinema.
-
D.
Sara Braun
Sara Braun was a prominent late 19th- and early 20th-century businesswoman and philanthropist in Chilean Patagonia, known for her influential role in regional development and society.
-
E.
Sara Henry
Sara Henry is known as the wife of American voice actor and comedian Mike Henry, recognized for his work on shows like Family Guy.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d1f9cf6c81909a6b9e9b9d0a79fe |
completed | April 7, 2026, 9:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89f25c16c8190a17dc19e3e1b197a |
completed | April 10, 2026, 6:56 a.m. |
| NEDg | Description generation | batch_69d8a2b0d8c88190a1a64bd2bbacabbe |
completed | April 10, 2026, 7:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d8a6560ddc81909d540f78a9413b3e |
completed | April 10, 2026, 7:27 a.m. |
Created at: April 6, 2026, 11:17 a.m.