Triple
T13990616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kung Fu |
E336562
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Ed Spielman |
E908893
|
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: Ed Spielman | Statement: [Kung Fu, creator, Ed Spielman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ed Spielman Context triple: [Kung Fu, creator, Ed Spielman]
-
A.
Ed Spielman
chosen
Ed Spielman is an American screenwriter and television producer best known for creating the martial arts series "Kung Fu" and producing various action-adventure TV shows.
-
B.
Eli Spielman
Eli Spielman is a writer known for co-authoring work with renowned sportscaster Jim Nantz.
-
C.
Ian Kahn
Ian Kahn is an American actor best known for playing George Washington on the television series "Turn: Washington's Spies."
-
D.
Richard Lipton
Richard Lipton is an American computer scientist known for his influential work in theoretical computer science and cryptography, including contributions to complexity theory and algorithm design.
-
E.
Michael Lehmann
Michael Lehmann is an American film and television director best known for the dark comedy "Heathers" and various other Hollywood comedies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb22e388190904fc87765176c91 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbac98ca448190b585ef69a4e4bfca |
completed | May 6, 2026, 9:03 p.m. |
Created at: April 9, 2026, 10:18 p.m.