Triple
T20589476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Jungle Book 2 |
E505876
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | David Reynolds |
—
|
NE NERFINISHED |
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: David Reynolds | Statement: [The Jungle Book 2, screenwriter, David Reynolds]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Reynolds Context triple: [The Jungle Book 2, screenwriter, David Reynolds]
-
A.
David Reynolds
chosen
David Reynolds is an American screenwriter best known for his work on acclaimed animated films such as Pixar's "Finding Nemo."
-
B.
Paul Reynolds
Paul Reynolds is a New Zealand politician and former chief executive of Telecom New Zealand, known for leading the company through major structural and technological changes.
-
C.
Paul Reynolds
Paul Reynolds is a British actor best known for his role in the television series "Press Gang."
-
D.
William Reynolds
William Reynolds is a film editor known for his work on the 1960 adventure drama "Wild River."
-
E.
William Reynolds
William Reynolds is a film editor best known for his work on the classic crime drama "The Godfather."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b9669c8190b8e81fc72817d42c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a979e4a48190a948165fb0f3b265 |
completed | April 20, 2026, 10:32 p.m. |
Created at: April 16, 2026, 11:40 a.m.