Triple
T14235141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Son in Law |
E352857
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Susan McMartin
Susan McMartin is an American television and film writer best known for her work on series such as "Mom" and "Two and a Half Men."
|
E1180792
|
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: Susan McMartin | Statement: [Son in Law, screenwriter, Susan McMartin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Susan McMartin Context triple: [Son in Law, screenwriter, Susan McMartin]
-
A.
Susan Dougan
Susan Dougan is a Vincentian public figure who serves as the Governor-General and de facto representative of the British monarch in Saint Vincent and the Grenadines.
-
B.
Ann McMillan
Ann McMillan is the daughter of American physicist and Nobel laureate Edwin McMillan.
-
C.
Susan Egan
Susan Egan is an American actress and singer best known for originating the role of Belle in Broadway’s Beauty and the Beast and voicing Megara in Disney’s animated film Hercules.
-
D.
Susan Wakley
Susan Wakley is best known as the wife of Pro Football Hall of Famer and longtime sportscaster Merlin Olsen.
-
E.
Lisa McGrillis
Lisa McGrillis is a British actress known for her work in television, film, and theatre, including roles in series like "Inspector George Gently" and "Mum."
- 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: Susan McMartin Triple: [Son in Law, screenwriter, Susan McMartin]
Generated description
Susan McMartin is an American television and film writer best known for her work on series such as "Mom" and "Two and a Half Men."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Susan McMartin Target entity description: Susan McMartin is an American television and film writer best known for her work on series such as "Mom" and "Two and a Half Men."
-
A.
Susan Dougan
Susan Dougan is a Vincentian public figure who serves as the Governor-General and de facto representative of the British monarch in Saint Vincent and the Grenadines.
-
B.
Ann McMillan
Ann McMillan is the daughter of American physicist and Nobel laureate Edwin McMillan.
-
C.
Susan Egan
Susan Egan is an American actress and singer best known for originating the role of Belle in Broadway’s Beauty and the Beast and voicing Megara in Disney’s animated film Hercules.
-
D.
Susan Wakley
Susan Wakley is best known as the wife of Pro Football Hall of Famer and longtime sportscaster Merlin Olsen.
-
E.
Lisa McGrillis
Lisa McGrillis is a British actress known for her work in television, film, and theatre, including roles in series like "Inspector George Gently" and "Mum."
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de62411c888190a154acd56fe3fcaf |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa9280ed081908030a4bbea80398c |
completed | May 9, 2026, 9:37 p.m. |
| NEDg | Description generation | batch_69ffaa5d8860819090dc3e36a32dfb3a |
completed | May 9, 2026, 9:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffaab523dc8190bbf3cd271852493b |
completed | May 9, 2026, 9:44 p.m. |
Created at: April 10, 2026, 1:07 a.m.