Triple
T23107523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lozanne |
E576215
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object |
Lozanne station
Lozanne station is a local railway stop in the commune of Lozanne in eastern France, serving regional passenger trains and connecting the town to the surrounding Lyon metropolitan area.
|
E1378515
|
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: Lozanne station | Statement: [Lozanne, hasRailwayStation, Lozanne station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lozanne station Context triple: [Lozanne, hasRailwayStation, Lozanne station]
-
A.
Novza station
Novza station is a metro station on the Tashkent Metro system in Tashkent, Uzbekistan.
-
B.
Matrand Station
Matrand Station is a small railway station serving the village of Matrand in Eidskog Municipality in Innlandet county, Norway.
-
C.
Ghrmaghele station
Ghrmaghele station is a metro station on the Tbilisi Metro system in Tbilisi, Georgia.
-
D.
Vavin station
Vavin station is a Paris Métro station serving the Montparnasse area on the Left Bank.
-
E.
Tassin station
Tassin station is a local railway station serving the commune of Tassin-la-Demi-Lune in the Lyon metropolitan area of France.
- 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: Lozanne station Triple: [Lozanne, hasRailwayStation, Lozanne station]
Generated description
Lozanne station is a local railway stop in the commune of Lozanne in eastern France, serving regional passenger trains and connecting the town to the surrounding Lyon metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lozanne station Target entity description: Lozanne station is a local railway stop in the commune of Lozanne in eastern France, serving regional passenger trains and connecting the town to the surrounding Lyon metropolitan area.
-
A.
Novza station
Novza station is a metro station on the Tashkent Metro system in Tashkent, Uzbekistan.
-
B.
Matrand Station
Matrand Station is a small railway station serving the village of Matrand in Eidskog Municipality in Innlandet county, Norway.
-
C.
Ghrmaghele station
Ghrmaghele station is a metro station on the Tbilisi Metro system in Tbilisi, Georgia.
-
D.
Vavin station
Vavin station is a Paris Métro station serving the Montparnasse area on the Left Bank.
-
E.
Tassin station
chosen
Tassin station is a local railway station serving the commune of Tassin-la-Demi-Lune in the Lyon metropolitan area of France.
- F. None of above.
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_69e245f4af548190898d434a64a1e774 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e0c7b9c8190b1160485eae87c9b |
completed | April 29, 2026, 4:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c23e606cc8190b0f51e22318250ad |
completed | May 19, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_6a0c28083b548190b8773691b6cc1e39 |
completed | May 19, 2026, 9:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c28f3c9288190b9ee0e5c4911ee9b |
completed | May 19, 2026, 9:10 a.m. |
Created at: April 17, 2026, 3:58 p.m.