Triple
T9741219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wyatt Isabelle Kutcher |
E236188
|
entity |
| Predicate | middleName |
P143
|
FINISHED |
| Object |
Isabelle
Isabelle is a given name commonly used as a feminine first or middle name in various cultures.
|
E386747
|
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: Isabelle | Statement: [Wyatt Isabelle Kutcher, middleName, Isabelle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabelle Context triple: [Wyatt Isabelle Kutcher, middleName, Isabelle]
-
A.
Isabelle
Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
-
B.
Isabel
Isabel is a feminine given name of Spanish origin, widely used in Spanish- and Portuguese-speaking countries and borne by numerous notable historical and contemporary figures.
-
C.
Isabel
Isabel is a Spanish historical drama television series centered on the life and reign of Queen Isabella I of Castile.
-
D.
Isabella
Isabella was an English princess of the 13th century, daughter of King John of England, who became Lady de Coucy through marriage into the French nobility.
-
E.
Isabella
Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
- 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: Isabelle Triple: [Wyatt Isabelle Kutcher, middleName, Isabelle]
Generated description
Isabelle is a given name commonly used as a feminine first or middle name in various cultures.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Isabelle Target entity description: Isabelle is a given name commonly used as a feminine first or middle name in various cultures.
-
A.
Isabelle
chosen
Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
-
B.
Isabel
Isabel is a feminine given name of Spanish origin, widely used in Spanish- and Portuguese-speaking countries and borne by numerous notable historical and contemporary figures.
-
C.
Isabel
Isabel is a Spanish historical drama television series centered on the life and reign of Queen Isabella I of Castile.
-
D.
Isabella
Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
-
E.
Isabella
Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
- F. None of above.
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_69ca84d3e24481908a476e2231123cf9 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f2af3e48190b83a442cd0e84062 |
completed | April 1, 2026, 10:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1afe974608190874e2aba2189de80 |
completed | April 5, 2026, 12:42 a.m. |
| NEDg | Description generation | batch_69d1b08ba1f48190830852f9d60e3368 |
completed | April 5, 2026, 12:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1b124659481909e7a2ecaf01d8a50 |
completed | April 5, 2026, 12:47 a.m. |
Created at: March 30, 2026, 8:23 p.m.