Triple
T3362695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Cox |
E70759
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Margaret Cox
Margaret Cox is known as the daughter of British physicist and science communicator Brian Cox.
|
E502711
|
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: Margaret Cox | Statement: [Brian Cox, hasChild, Margaret Cox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margaret Cox Context triple: [Brian Cox, hasChild, Margaret Cox]
-
A.
Margaret Crocker
Margaret Crocker was a 19th-century Sacramento philanthropist best known for donating her late husband's extensive art collection and endowing what became the Crocker Art Museum.
-
B.
Maureen Cox
Maureen Cox was a British hairdresser best known as the first wife of Beatles drummer Ringo Starr.
-
C.
Marjorie Reynolds
Marjorie Reynolds was an American film and television actress best known for her roles in classic 1940s movies and early TV series.
-
D.
Margaret Gibson
Margaret Gibson was the wife of American actor Noah Beery, associated with the early Hollywood film era.
-
E.
Margaret Haley
Margaret Haley was an influential American educator and labor activist who championed teachers' rights and helped pioneer the modern teachers' union movement.
- 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: Margaret Cox Triple: [Brian Cox, hasChild, Margaret Cox]
Generated description
Margaret Cox is known as the daughter of British physicist and science communicator Brian Cox.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Margaret Cox Target entity description: Margaret Cox is known as the daughter of British physicist and science communicator Brian Cox.
-
A.
Margaret Crocker
Margaret Crocker was a 19th-century Sacramento philanthropist best known for donating her late husband's extensive art collection and endowing what became the Crocker Art Museum.
-
B.
Maureen Cox
Maureen Cox was a British hairdresser best known as the first wife of Beatles drummer Ringo Starr.
-
C.
Marjorie Reynolds
Marjorie Reynolds was an American film and television actress best known for her roles in classic 1940s movies and early TV series.
-
D.
Margaret Gibson
Margaret Gibson was the wife of American actor Noah Beery, associated with the early Hollywood film era.
-
E.
Margaret Haley
Margaret Haley was an influential American educator and labor activist who championed teachers' rights and helped pioneer the modern teachers' union movement.
- 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_69ad85a660c48190998489309a3b4869 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb26a5a508190948932769de0dbb3 |
completed | March 8, 2026, 5:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beef70e6048190b0e5659a59634d5d |
completed | March 21, 2026, 7:20 p.m. |
| NEDg | Description generation | batch_69bef031c0e881908a4a427788db618f |
completed | March 21, 2026, 7:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bef086d9d08190813cd1bd8fdec5d3 |
completed | March 21, 2026, 7:24 p.m. |
Created at: March 8, 2026, 3:13 p.m.