Triple
T2210480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | What Maisie Knew |
E50904
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Maisie Farange
Maisie Farange is the perceptive child protagonist of Henry James’s novel "What Maisie Knew," whose experiences reveal the emotional fallout of her parents’ bitter divorce.
|
E245592
|
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: Maisie Farange | Statement: [What Maisie Knew, mainCharacter, Maisie Farange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maisie Farange Context triple: [What Maisie Knew, mainCharacter, Maisie Farange]
-
A.
Marylou
Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
-
B.
Felicia
Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
-
C.
Zibelle
Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
-
D.
Elsie
Elsie is a fictional character from the post-apocalyptic virtual reality game "After the Fall."
-
E.
Elsie
Elsie is the internal codename Apple used for the Macintosh LC personal computer during its development.
- 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: Maisie Farange Triple: [What Maisie Knew, mainCharacter, Maisie Farange]
Generated description
Maisie Farange is the perceptive child protagonist of Henry James’s novel "What Maisie Knew," whose experiences reveal the emotional fallout of her parents’ bitter divorce.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maisie Farange Target entity description: Maisie Farange is the perceptive child protagonist of Henry James’s novel "What Maisie Knew," whose experiences reveal the emotional fallout of her parents’ bitter divorce.
-
A.
Marylou
Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
-
B.
Felicia
Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
-
C.
Zibelle
Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
-
D.
Elsie
Elsie is a fictional character from the post-apocalyptic virtual reality game "After the Fall."
-
E.
Elsie
Elsie is the internal codename Apple used for the Macintosh LC personal computer during its development.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfeb889081908cddf58a57b216df |
completed | March 7, 2026, 6:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae655045d081909b8294ec706e0814 |
completed | March 9, 2026, 6:14 a.m. |
| NEDg | Description generation | batch_69ae662f689881908ecd76952b78f863 |
completed | March 9, 2026, 6:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae668ef8bc819085ed1c83f447d396 |
completed | March 9, 2026, 6:19 a.m. |
Created at: March 4, 2026, 7:46 p.m.