Triple
T8557528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Line (DART) |
E202609
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Kiest Station
Kiest Station is a Dallas Area Rapid Transit (DART) light rail stop on the Blue Line serving the Kiest Boulevard area in Dallas, Texas.
|
E743308
|
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: Kiest Station | Statement: [Blue Line (DART), hasStation, Kiest Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiest Station Context triple: [Blue Line (DART), hasStation, Kiest Station]
-
A.
Kampen station
Kampen station is a railway station in the city of Kampen in the Netherlands, serving as a local stop on the regional rail network.
-
B.
Rommen station
Rommen station is a metro stop in Oslo, Norway, located in the Groruddalen area and integrated into the city's rapid transit network.
-
C.
Kedzie station
Kedzie station is a Chicago 'L' rapid transit stop on the Brown Line serving the city's Northwest Side.
-
D.
Maliebaanstation
Maliebaanstation is a historic former railway station in Utrecht, the Netherlands, now best known as the home of the Dutch Railway Museum.
-
E.
Tulpehocken station
Tulpehocken station is a historic SEPTA Regional Rail stop in Philadelphia, Pennsylvania, serving the Chestnut Hill West Line.
- 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: Kiest Station Triple: [Blue Line (DART), hasStation, Kiest Station]
Generated description
Kiest Station is a Dallas Area Rapid Transit (DART) light rail stop on the Blue Line serving the Kiest Boulevard area in Dallas, Texas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kiest Station Target entity description: Kiest Station is a Dallas Area Rapid Transit (DART) light rail stop on the Blue Line serving the Kiest Boulevard area in Dallas, Texas.
-
A.
Kampen station
Kampen station is a railway station in the city of Kampen in the Netherlands, serving as a local stop on the regional rail network.
-
B.
Rommen station
Rommen station is a metro stop in Oslo, Norway, located in the Groruddalen area and integrated into the city's rapid transit network.
-
C.
Kedzie station
Kedzie station is a Chicago 'L' rapid transit stop on the Brown Line serving the city's Northwest Side.
-
D.
Maliebaanstation
Maliebaanstation is a historic former railway station in Utrecht, the Netherlands, now best known as the home of the Dutch Railway Museum.
-
E.
Tulpehocken station
Tulpehocken station is a historic SEPTA Regional Rail stop in Philadelphia, Pennsylvania, serving the Chestnut Hill West Line.
- 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_69ca8326e6c881908ff720d6abaebdc5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe946d1408190adc7dfb7b2173f9d |
completed | March 31, 2026, 3:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce89455dcc819088bdf5a2f653da17 |
completed | April 2, 2026, 3:20 p.m. |
| NEDg | Description generation | batch_69ce8a9ba0448190ae7637f24b8a8032 |
completed | April 2, 2026, 3:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce8bda33548190a8f6985a48d65a39 |
completed | April 2, 2026, 3:31 p.m. |
Created at: March 30, 2026, 6:20 p.m.