Triple
T964392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riga |
E20805
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Centrs
Centrs is the central district of Riga, Latvia, known for its historic architecture, cultural institutions, and commercial activity.
|
E113539
|
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: Centrs | Statement: [Riga, hasDistrict, Centrs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Centrs Context triple: [Riga, hasDistrict, Centrs]
-
A.
Sentrum
Sentrum is the central district of Oslo, Norway, which hosts some of the University of Oslo’s urban campus facilities.
-
B.
Mitte
Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
-
C.
Innenstadt
Innenstadt is the central urban district of Frankfurt am Main, known as the city’s historic core and primary commercial area.
-
D.
Stadtmitte
Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
-
E.
Lübars
Lübars is a historic, village-like district in Berlin’s Reinickendorf borough, known for its rural character, fields, and preserved traditional architecture within the city.
- 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: Centrs Triple: [Riga, hasDistrict, Centrs]
Generated description
Centrs is the central district of Riga, Latvia, known for its historic architecture, cultural institutions, and commercial activity.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Centrs Target entity description: Centrs is the central district of Riga, Latvia, known for its historic architecture, cultural institutions, and commercial activity.
-
A.
Sentrum
Sentrum is the central district of Oslo, Norway, which hosts some of the University of Oslo’s urban campus facilities.
-
B.
Mitte
Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
-
C.
Innenstadt
Innenstadt is the central urban district of Frankfurt am Main, known as the city’s historic core and primary commercial area.
-
D.
Stadtmitte
Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
-
E.
Lübars
Lübars is a historic, village-like district in Berlin’s Reinickendorf borough, known for its rural character, fields, and preserved traditional architecture within the city.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4303e5881909d101d11f9732c75 |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac11a82bfc81908a30b19d4ecc25f1 |
completed | March 7, 2026, 11:53 a.m. |
| NEDg | Description generation | batch_69ac12e7384881908211de8a4092b3c0 |
completed | March 7, 2026, 11:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac135f4b9c8190b45955bc9ef65608 |
completed | March 7, 2026, noon |
Created at: March 1, 2026, 7:40 p.m.