Triple
T13358061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vallendar |
E318745
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Gutenberg
Gutenberg is a small district or locality within the German town of Vallendar.
|
E1037610
|
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: Gutenberg | Statement: [Vallendar, hasSubdivision, Gutenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gutenberg Context triple: [Vallendar, hasSubdivision, Gutenberg]
-
A.
Gutenberg
Gutenberg is the block-based content editor introduced in WordPress to enable more flexible, visual page and post creation.
-
B.
Guttenberg
Guttenberg is a small municipality in the Kulmbach district of Bavaria, Germany, known for its rural setting and traditional Franconian character.
-
C.
Guttenberg
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.
-
D.
Gutenberg the Geek
Gutenberg the Geek is a book by media theorist Jeff Jarvis that reinterprets Johannes Gutenberg as an early tech entrepreneur to draw parallels between the printing revolution and the digital age.
-
E.
The Gutenberg Elegies
The Gutenberg Elegies is a collection of essays by Sven Birkerts that reflects on the cultural and intellectual consequences of the shift from print to digital media.
- 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: Gutenberg Triple: [Vallendar, hasSubdivision, Gutenberg]
Generated description
Gutenberg is a small district or locality within the German town of Vallendar.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gutenberg Target entity description: Gutenberg is a small district or locality within the German town of Vallendar.
-
A.
Gutenberg
Gutenberg is the block-based content editor introduced in WordPress to enable more flexible, visual page and post creation.
-
B.
Guttenberg
Guttenberg is a small municipality in the Kulmbach district of Bavaria, Germany, known for its rural setting and traditional Franconian character.
-
C.
Guttenberg
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.
-
D.
Gutenberg the Geek
Gutenberg the Geek is a book by media theorist Jeff Jarvis that reinterprets Johannes Gutenberg as an early tech entrepreneur to draw parallels between the printing revolution and the digital age.
-
E.
The Gutenberg Elegies
The Gutenberg Elegies is a collection of essays by Sven Birkerts that reflects on the cultural and intellectual consequences of the shift from print to digital media.
- F. None of above. chosen
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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da62887e588190bd7241c720a112a2 |
completed | April 11, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f72677b2a48190aad30f3ee6cacefb |
completed | May 3, 2026, 10:41 a.m. |
| NEDg | Description generation | batch_69f727512c94819091985c7942f40b31 |
completed | May 3, 2026, 10:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f72b77d650819092c02f6488b2cfb2 |
completed | May 3, 2026, 11:03 a.m. |
Created at: April 9, 2026, 9:32 p.m.