Triple
T15399994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teddy Reed |
E368286
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Teddy |
unclear NED1
|
NE FINISHED |
How this triple was built (2 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: [Teddy Reed, givenName, Teddy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teddy Context triple: [Teddy Reed, givenName, 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 a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
-
C.
Teddy
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
-
D.
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.
-
E.
Teddy
Teddy is a short story by J.D. Salinger that follows a spiritually precocious child whose philosophical insights unsettle the adults around him.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
Provenance (3 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8d89e08190b7cae778d89fb5e1 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13567e3481908eb6293c6af35f3a |
completed | May 9, 2026, 10:58 a.m. |
Created at: April 10, 2026, 3:19 a.m.