Triple
T6164837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fry |
E137530
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Caroline Fry
Caroline Fry was a 19th-century English Christian writer and moralist known for her religious essays and devotional works.
|
E586955
|
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: Caroline Fry | Statement: [Fry, hasNotableBearer, Caroline Fry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caroline Fry Context triple: [Fry, hasNotableBearer, Caroline Fry]
-
A.
Caroline Graham
Caroline Graham is a British crime novelist best known for creating the Chief Inspector Barnaby books that inspired the television series "Midsomer Murders."
-
B.
Caroline Baron
Caroline Baron is an American film producer known for her work on acclaimed independent and studio films, including the biographical drama "Capote."
-
C.
Caroline Black
Caroline Black is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Black.
-
D.
Caroline Milmoe
Caroline Milmoe is a British actress known for her work in film and television, particularly in the 1980s and 1990s.
-
E.
Caroline Ross
Caroline Ross is a film editor known for her work on the science fiction movie "Starship Troopers."
- 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: Caroline Fry Triple: [Fry, hasNotableBearer, Caroline Fry]
Generated description
Caroline Fry was a 19th-century English Christian writer and moralist known for her religious essays and devotional works.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Caroline Fry Target entity description: Caroline Fry was a 19th-century English Christian writer and moralist known for her religious essays and devotional works.
-
A.
Caroline Graham
Caroline Graham is a British crime novelist best known for creating the Chief Inspector Barnaby books that inspired the television series "Midsomer Murders."
-
B.
Caroline Baron
Caroline Baron is an American film producer known for her work on acclaimed independent and studio films, including the biographical drama "Capote."
-
C.
Caroline Black
Caroline Black is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Black.
-
D.
Caroline Milmoe
Caroline Milmoe is a British actress known for her work in film and television, particularly in the 1980s and 1990s.
-
E.
Caroline Ross
Caroline Ross is a film editor known for her work on the science fiction movie "Starship Troopers."
- 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_69c008a54fc88190b6ce4416490ca79d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d6225bc819097707be620681e7b |
completed | March 22, 2026, 9:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c603dd70b48190844e513246930b53 |
completed | March 27, 2026, 4:13 a.m. |
| NEDg | Description generation | batch_69c606751c60819081f7ecf92131606a |
completed | March 27, 2026, 4:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c606ccace88190800e3621ac0a2fa5 |
completed | March 27, 2026, 4:25 a.m. |
Created at: March 22, 2026, 4:17 p.m.