Triple
T9550551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glen Ellyn station |
E230409
|
entity |
| Predicate | fareZone |
P844
|
FINISHED |
| Object |
Zone E
Zone E is a Metra commuter rail fare zone in the Chicago metropolitan area used to determine ticket prices based on distance traveled.
|
E805389
|
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: Zone E | Statement: [Glen Ellyn station, fareZone, Zone E]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zone E Context triple: [Glen Ellyn station, fareZone, Zone E]
-
A.
Zone D
Zone D is a designated commuter rail fare zone used to determine ticket prices for travel to and from Hinsdale station.
-
B.
Zone G
Zone G is a designated fare zone within the Glasgow Subway ticketing system that includes Kelvinbridge subway station.
-
C.
Zone 5
Zone 5 is an outer fare zone in the London public transport system used for calculating ticket and Travelcard prices.
-
D.
Zone 3
Zone 3 is one of the MBTA Commuter Rail’s outer fare zones used to set ticket prices for trips between Boston and its surrounding suburbs.
-
E.
Zone 3
Zone 3 is one of the concentric public transport fare zones in the Île-de-France region surrounding central Paris.
- 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: Zone E Triple: [Glen Ellyn station, fareZone, Zone E]
Generated description
Zone E is a Metra commuter rail fare zone in the Chicago metropolitan area used to determine ticket prices based on distance traveled.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zone E Target entity description: Zone E is a Metra commuter rail fare zone in the Chicago metropolitan area used to determine ticket prices based on distance traveled.
-
A.
Zone D
Zone D is a designated commuter rail fare zone used to determine ticket prices for travel to and from Hinsdale station.
-
B.
Zone G
Zone G is a designated fare zone within the Glasgow Subway ticketing system that includes Kelvinbridge subway station.
-
C.
Zone 5
Zone 5 is an outer fare zone in the London public transport system used for calculating ticket and Travelcard prices.
-
D.
Zone 3
Zone 3 is one of the MBTA Commuter Rail’s outer fare zones used to set ticket prices for trips between Boston and its surrounding suburbs.
-
E.
Zone 3
Zone 3 is one of the concentric public transport fare zones in the Île-de-France region surrounding central Paris.
- 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_69ca847d3be8819099c9dad2a7e786f1 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9906bc90819086f105c453e63c83 |
completed | April 1, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c85b9208190acf98fa985b0f01f |
completed | April 4, 2026, 5:38 p.m. |
| NEDg | Description generation | batch_69d14d0c39c88190a705470104dc7b80 |
completed | April 4, 2026, 5:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d14d79065081908a4e619c71e0d359 |
completed | April 4, 2026, 5:42 p.m. |
Created at: March 30, 2026, 8:02 p.m.