Triple
T15855533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ōtsu |
E384443
|
entity |
| Predicate | hasRailStation |
P726
|
FINISHED |
| Object |
Zeze Station
Zeze Station is a railway station serving the city of Ōtsu in Shiga Prefecture, Japan.
|
E1179528
|
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: Zeze Station | Statement: [Ōtsu, hasRailStation, Zeze Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zeze Station Context triple: [Ōtsu, hasRailStation, Zeze Station]
-
A.
Jaren Station
Jaren Station is a local railway station serving the village of Jaren in Gran municipality in Innlandet county, Norway.
-
B.
Tumba station
Tumba station is a railway station in Tumba, Sweden, serving as a local commuter hub on the Stockholm commuter rail network.
-
C.
Cilebut Station
Cilebut Station is a commuter rail station in West Java, Indonesia, serving the KRL Jabodetabek line between Bogor and Jakarta.
-
D.
Kwinana station
Kwinana station is a suburban passenger railway station in Perth, Western Australia, serving the Kwinana area on the Mandurah line.
-
E.
Luz Station
Luz Station is a historic railway station and major transportation hub in São Paulo, Brazil, known for its distinctive architecture and cultural significance.
- 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: Zeze Station Triple: [Ōtsu, hasRailStation, Zeze Station]
Generated description
Zeze Station is a railway station serving the city of Ōtsu in Shiga Prefecture, Japan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zeze Station Target entity description: Zeze Station is a railway station serving the city of Ōtsu in Shiga Prefecture, Japan.
-
A.
Jaren Station
Jaren Station is a local railway station serving the village of Jaren in Gran municipality in Innlandet county, Norway.
-
B.
Tumba station
Tumba station is a railway station in Tumba, Sweden, serving as a local commuter hub on the Stockholm commuter rail network.
-
C.
Cilebut Station
Cilebut Station is a commuter rail station in West Java, Indonesia, serving the KRL Jabodetabek line between Bogor and Jakarta.
-
D.
Kwinana station
Kwinana station is a suburban passenger railway station in Perth, Western Australia, serving the Kwinana area on the Mandurah line.
-
E.
Luz Station
Luz Station is a historic railway station and major transportation hub in São Paulo, Brazil, known for its distinctive architecture and cultural significance.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e14caf6ae481909ae1385cb4548612 |
completed | April 16, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa14977408190815ef02cc54075cc |
completed | May 9, 2026, 9:04 p.m. |
| NEDg | Description generation | batch_69ffa41a86ec8190b46d541965ecf26e |
completed | May 9, 2026, 9:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffa496f3e48190b8dc82bece548aec |
completed | May 9, 2026, 9:18 p.m. |
Created at: April 10, 2026, 4:50 a.m.