Triple
T23442341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bob Belcher |
E565436
|
entity |
| Predicate | neighborOf |
P350
|
FINISHED |
| Object |
Teddy
Teddy is the friendly, blue-collar handyman and loyal regular at Bob's Burgers in the animated TV series "Bob's Burgers."
|
E565438
|
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: Teddy | Statement: [Bob Belcher, neighborOf, Teddy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teddy Context triple: [Bob Belcher, neighborOf, Teddy]
-
A.
Teddy
Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
-
B.
Teddy
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
-
C.
Teddy
Teddy is the young English boy in Rudyard Kipling’s story “Rikki-Tikki-Tavi,” whose life is saved from deadly cobras by the brave mongoose.
-
D.
Teddy
Teddy is a short story by J.D. Salinger that follows a spiritually precocious child whose philosophical insights unsettle the adults around him.
-
E.
Teddy
Teddy is the metamorphmagus son of Remus Lupin and Nymphadora Tonks in the Harry Potter series, known for being orphaned during the Battle of Hogwarts and later raised by his grandmother and the Weasley-Potter family.
- 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: Teddy Triple: [Bob Belcher, neighborOf, Teddy]
Generated description
Teddy is the friendly, blue-collar handyman and loyal regular at Bob's Burgers in the animated TV series "Bob's Burgers."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Teddy Target entity description: Teddy is the friendly, blue-collar handyman and loyal regular at Bob's Burgers in the animated TV series "Bob's Burgers."
-
A.
Teddy
chosen
Teddy is a recurring character on the animated TV show "Bob's Burgers," known as the Belcher family's loyal but somewhat bumbling handyman and regular customer.
-
B.
Teddy
Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
-
C.
Teddy
Teddy is a superhero character associated with Marvel's Future Foundation, often depicted as a young, gifted member involved in advanced scientific and cosmic adventures.
-
D.
Teddy
Teddy is the metamorphmagus son of Remus Lupin and Nymphadora Tonks in the Harry Potter series, known for being orphaned during the Battle of Hogwarts and later raised by his grandmother and the Weasley-Potter family.
-
E.
Teddy
Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64654e88190b530958b27b32412 |
completed | April 29, 2026, 6:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c67b583fc8190941de49b824f9c98 |
completed | May 19, 2026, 1:37 p.m. |
| NEDg | Description generation | batch_6a0c7217edc881909f582f0c726d7e61 |
completed | May 19, 2026, 2:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c76f0f4bc81909912d2196e969cf1 |
completed | May 19, 2026, 2:42 p.m. |
Created at: April 17, 2026, 5:51 p.m.