Triple
T14397468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Guttenberg |
E356986
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Guttenberg |
E356986
|
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: Guttenberg | Statement: [Steve Guttenberg, familyName, Guttenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guttenberg Context triple: [Steve Guttenberg, familyName, Guttenberg]
-
A.
Guttenberg
chosen
Guttenberg is a surname most famously associated with American actor and comedian Steve Guttenberg, known for his roles in 1980s films such as the Police Academy series.
-
B.
Guttenberg
Guttenberg is a small municipality in the Kulmbach district of Bavaria, Germany, known for its rural setting and traditional Franconian character.
-
C.
Gutenberg
Gutenberg is the block-based content editor introduced in WordPress to enable more flexible, visual page and post creation.
-
D.
Gutenberg
Gutenberg is a small district or locality within the German town of Vallendar.
-
E.
Johannes Gutenberg
Johannes Gutenberg was a 15th-century German inventor and printer credited with introducing movable-type printing to Europe, revolutionizing the spread of information.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90826f908190b3969af9b7cf922f |
completed | April 14, 2026, 7:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd551cbdb08190a9ea53e607f2555b |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:17 a.m.