Triple
T12638798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northeim district |
E301836
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Uslar
Uslar is a small town in Lower Saxony, Germany, known for its location in the Weser Uplands and its traditional half-timbered architecture.
|
E994484
|
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: Uslar | Statement: [Northeim district, containsTown, Uslar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uslar Context triple: [Northeim district, containsTown, Uslar]
-
A.
Uslan
Uslan is a surname most notably associated with Michael Uslan, the American film producer and comic book historian behind the modern Batman movie franchise.
-
B.
Usholta
Usholta is a small settlement located in the mountainous Racha region of northwestern Georgia, known for its remote rural character.
-
C.
Uzal
Uzal is a masculine given name of Hebrew origin that appears in biblical genealogies and has been used by various historical figures.
-
D.
Waras
Waras is a significant town in Afghanistan’s central highland region of Hazarajat, serving as an important local hub for the surrounding Hazara communities.
-
E.
Ushba
Ushba is a striking double-summited peak in the Caucasus Mountains of Georgia, renowned among climbers for its technical difficulty and dramatic, spire-like profile.
- 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: Uslar Triple: [Northeim district, containsTown, Uslar]
Generated description
Uslar is a small town in Lower Saxony, Germany, known for its location in the Weser Uplands and its traditional half-timbered architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Uslar Target entity description: Uslar is a small town in Lower Saxony, Germany, known for its location in the Weser Uplands and its traditional half-timbered architecture.
-
A.
Uslan
Uslan is a surname most notably associated with Michael Uslan, the American film producer and comic book historian behind the modern Batman movie franchise.
-
B.
Usholta
Usholta is a small settlement located in the mountainous Racha region of northwestern Georgia, known for its remote rural character.
-
C.
Uzal
Uzal is a masculine given name of Hebrew origin that appears in biblical genealogies and has been used by various historical figures.
-
D.
Waras
Waras is a significant town in Afghanistan’s central highland region of Hazarajat, serving as an important local hub for the surrounding Hazara communities.
-
E.
Ushba
Ushba is a striking double-summited peak in the Caucasus Mountains of Georgia, renowned among climbers for its technical difficulty and dramatic, spire-like profile.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961499de08190bdba66ca40b021be |
completed | April 10, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f668754acc8190b5585dbd35387867 |
completed | May 2, 2026, 9:11 p.m. |
| NEDg | Description generation | batch_69f6697d8ac88190b4ead9ce47f3a705 |
completed | May 2, 2026, 9:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f66a2fa350819091f12e16d59c2278 |
completed | May 2, 2026, 9:18 p.m. |
Created at: April 9, 2026, 5:16 p.m.