Triple
T14185709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rotterdam Metro |
E351568
|
entity |
| Predicate | majorStation |
P1071
|
FINISHED |
| Object |
Blaak
Blaak is a central transport hub and urban square in Rotterdam, known for its metro and train station near landmarks like the Cube Houses and Markthal.
|
E1086257
|
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: Blaak | Statement: [Rotterdam Metro, majorStation, Blaak]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blaak Context triple: [Rotterdam Metro, majorStation, Blaak]
-
A.
Bleckede
Bleckede is a small town in Lower Saxony, Germany, situated on the Elbe River and known for its historic architecture and natural surroundings.
-
B.
Blau
The Blau is a small river in the German state of Baden-Württemberg that flows through the city of Blaustein before joining the Danube.
-
C.
Bleik
Bleik is a small coastal village on the island of Andøya in northern Norway, known for its long sandy beach and proximity to rich seabird colonies.
-
D.
Blaauw
Blaauw is a Dutch surname most notably associated with Gerrit Blaauw, a pioneering computer architect involved in the design of early IBM systems.
-
E.
Blacker
Blacker is a comparative form of the color term "black," indicating a greater degree of darkness or blackness.
- 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: Blaak Triple: [Rotterdam Metro, majorStation, Blaak]
Generated description
Blaak is a central transport hub and urban square in Rotterdam, known for its metro and train station near landmarks like the Cube Houses and Markthal.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Blaak Target entity description: Blaak is a central transport hub and urban square in Rotterdam, known for its metro and train station near landmarks like the Cube Houses and Markthal.
-
A.
Bleckede
Bleckede is a small town in Lower Saxony, Germany, situated on the Elbe River and known for its historic architecture and natural surroundings.
-
B.
Blau
The Blau is a small river in the German state of Baden-Württemberg that flows through the city of Blaustein before joining the Danube.
-
C.
Bleik
Bleik is a small coastal village on the island of Andøya in northern Norway, known for its long sandy beach and proximity to rich seabird colonies.
-
D.
Blaauw
Blaauw is a Dutch surname most notably associated with Gerrit Blaauw, a pioneering computer architect involved in the design of early IBM systems.
-
E.
Blacker
Blacker is a comparative form of the color term "black," indicating a greater degree of darkness or blackness.
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61cd5778819092a03597bcdcc182 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd19433dc08190b4d2f1aef1b2d67d |
completed | May 7, 2026, 10:59 p.m. |
| NEDg | Description generation | batch_69fd1ad65b608190b325c82347a3d8b3 |
completed | May 7, 2026, 11:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd1b68e8288190a9e9e3d855ed2719 |
completed | May 7, 2026, 11:08 p.m. |
Created at: April 10, 2026, 1:03 a.m.