Triple
T8663508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landkreis Reutlingen |
E205604
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Zwiefalten
Zwiefalten is a small historic town and former monastic center in the state of Baden-Württemberg in southern Germany.
|
E749254
|
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: Zwiefalten | Statement: [Landkreis Reutlingen, contains, Zwiefalten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zwiefalten Context triple: [Landkreis Reutlingen, contains, Zwiefalten]
-
A.
Zweibund
Zweibund was the German term for the Dual Alliance, a key late 19th-century military and political alliance between Germany and Austria-Hungary that shaped pre–World War I European diplomacy.
-
B.
Zesgehuchten
Zesgehuchten was a former village and municipality in the Dutch province of North Brabant, now part of the city of Geldrop-Mierlo.
-
C.
Dvoynik
Dvoynik is the original Russian title of Fyodor Dostoevsky’s novella "The Double," a psychological work about a government clerk who encounters his uncanny doppelgänger.
-
D.
ZWEI
ZWEI is an early extensible text editor developed at MIT, notable as a precursor and influence on later Emacs implementations.
-
E.
De Dubbelen
De Dubbelen is an industrial estate in Veghel, Netherlands, known for hosting a range of manufacturing, logistics, and commercial businesses.
- 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: Zwiefalten Triple: [Landkreis Reutlingen, contains, Zwiefalten]
Generated description
Zwiefalten is a small historic town and former monastic center in the state of Baden-Württemberg in southern Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zwiefalten Target entity description: Zwiefalten is a small historic town and former monastic center in the state of Baden-Württemberg in southern Germany.
-
A.
Zweibund
Zweibund was the German term for the Dual Alliance, a key late 19th-century military and political alliance between Germany and Austria-Hungary that shaped pre–World War I European diplomacy.
-
B.
Zesgehuchten
Zesgehuchten was a former village and municipality in the Dutch province of North Brabant, now part of the city of Geldrop-Mierlo.
-
C.
Dvoynik
Dvoynik is the original Russian title of Fyodor Dostoevsky’s novella "The Double," a psychological work about a government clerk who encounters his uncanny doppelgänger.
-
D.
ZWEI
ZWEI is an early extensible text editor developed at MIT, notable as a precursor and influence on later Emacs implementations.
-
E.
De Dubbelen
De Dubbelen is an industrial estate in Veghel, Netherlands, known for hosting a range of manufacturing, logistics, and commercial businesses.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc489f7edc8190bde1b4dc09249207 |
completed | March 31, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecd0d95ec81908669ee35f0987be7 |
completed | April 2, 2026, 8:09 p.m. |
| NEDg | Description generation | batch_69cece8e5afc8190912b6eb9c80dd073 |
completed | April 2, 2026, 8:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cecf8ee4f48190bb901e35ade91b6c |
completed | April 2, 2026, 8:20 p.m. |
Created at: March 30, 2026, 6:30 p.m.