Triple
T7301823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Griffith Chaney |
E167874
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Joan London
Joan London was an American writer and the daughter of novelist Jack London, known for her memoirs and works about her father's life and legacy.
|
E661851
|
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: Joan London | Statement: [John Griffith Chaney, hasChild, Joan London]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joan London Context triple: [John Griffith Chaney, hasChild, Joan London]
-
A.
Joan Alison
Joan Alison was an American screenwriter best known for co-writing the classic 1942 film "Casablanca."
-
B.
Joan Barclay
Joan Barclay was an American film actress known for her numerous roles in low-budget Westerns and B-movies during the 1930s and 1940s.
-
C.
Joan Murray
Joan Murray was the wife of famed British World War II flying ace and double amputee Sir Douglas Bader.
-
D.
Lorraine Broughton
Lorraine Broughton is a highly skilled, stylish MI6 spy and lethal combatant who serves as the protagonist of the action thriller film "Atomic Blonde."
-
E.
Joan Holland
Joan Holland was an English noblewoman of the late 14th and early 15th centuries, connected to the royal House of York through her marriage to Edmund of Langley, 1st Duke of York.
- 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: Joan London Triple: [John Griffith Chaney, hasChild, Joan London]
Generated description
Joan London was an American writer and the daughter of novelist Jack London, known for her memoirs and works about her father's life and legacy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joan London Target entity description: Joan London was an American writer and the daughter of novelist Jack London, known for her memoirs and works about her father's life and legacy.
-
A.
Joan Alison
Joan Alison was an American screenwriter best known for co-writing the classic 1942 film "Casablanca."
-
B.
Joan Barclay
Joan Barclay was an American film actress known for her numerous roles in low-budget Westerns and B-movies during the 1930s and 1940s.
-
C.
Joan Murray
Joan Murray was the wife of famed British World War II flying ace and double amputee Sir Douglas Bader.
-
D.
Lorraine Broughton
Lorraine Broughton is a highly skilled, stylish MI6 spy and lethal combatant who serves as the protagonist of the action thriller film "Atomic Blonde."
-
E.
Joan Holland
Joan Holland was an English noblewoman of the late 14th and early 15th centuries, connected to the royal House of York through her marriage to Edmund of Langley, 1st Duke of York.
- 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_69c6888c820881909fc68f689fe1c251 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ebb09164819099c4479d48c1688a |
completed | March 27, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c810c167848190a70f4e43c19f5809 |
completed | March 28, 2026, 5:32 p.m. |
| NEDg | Description generation | batch_69c813960d548190a4797d3935b64cec |
completed | March 28, 2026, 5:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c813f87d388190ac49a036ea2acd1d |
completed | March 28, 2026, 5:46 p.m. |
Created at: March 27, 2026, 3:01 p.m.