Triple
T14253240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teletubbies |
E353321
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
Anne Wood
Anne Wood is a British television producer best known for creating the iconic preschool series "Teletubbies."
|
E1090862
|
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: Anne Wood | Statement: [Teletubbies, creator, Anne Wood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anne Wood Context triple: [Teletubbies, creator, Anne Wood]
-
A.
Anne Heywood
Anne Heywood is a British actress known for her film and television roles from the 1950s through the 1970s, often portraying strong, complex female characters.
-
B.
Anne Barnard
Anne Barnard is a journalist and foreign correspondent known for her reporting on conflict zones and Middle Eastern affairs, particularly for The New York Times.
-
C.
Alice Wood
Alice Wood is a central character in the 1973 mystery film "The Last of Sheila," involved in the intricate whodunit plot surrounding a deadly yacht party game.
-
D.
Emma Gillett
Emma Gillett was an American lawyer and pioneering advocate for women's legal education who co-founded what became the Washington College of Law.
-
E.
Anne Wheeler
Anne Wheeler is a fictional trapeze artist and acrobat featured in the musical film "The Greatest Showman."
- 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: Anne Wood Triple: [Teletubbies, creator, Anne Wood]
Generated description
Anne Wood is a British television producer best known for creating the iconic preschool series "Teletubbies."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anne Wood Target entity description: Anne Wood is a British television producer best known for creating the iconic preschool series "Teletubbies."
-
A.
Anne Heywood
Anne Heywood is a British actress known for her film and television roles from the 1950s through the 1970s, often portraying strong, complex female characters.
-
B.
Anne Barnard
Anne Barnard is a journalist and foreign correspondent known for her reporting on conflict zones and Middle Eastern affairs, particularly for The New York Times.
-
C.
Alice Wood
Alice Wood is a central character in the 1973 mystery film "The Last of Sheila," involved in the intricate whodunit plot surrounding a deadly yacht party game.
-
D.
Emma Gillett
Emma Gillett was an American lawyer and pioneering advocate for women's legal education who co-founded what became the Washington College of Law.
-
E.
Anne Wheeler
Anne Wheeler is a fictional trapeze artist and acrobat featured in the musical film "The Greatest Showman."
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6297f38c819090d7c7fd8bfa2e9e |
completed | April 14, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d127f0c81908dea42ca09c1abda |
completed | May 8, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69fd3e3d3e2c81909945253c26e19cee |
completed | May 8, 2026, 1:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3ebe4f008190aec72ed7e23c4cd4 |
completed | May 8, 2026, 1:39 a.m. |
Created at: April 10, 2026, 1:09 a.m.