Triple
T12314518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London fare zones |
E293566
|
entity |
| Predicate | includesZone |
P6793
|
FINISHED |
| Object |
Zone 3
Zone 3 is a mid-distance public transport fare zone in London covering various suburban residential and commercial areas outside the city center.
|
E979623
|
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 3 | Statement: [London fare zones, includesZone, Zone 3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zone 3 Context triple: [London fare zones, includesZone, Zone 3]
-
A.
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.
-
B.
Zone 3
Zone 3 is one of the concentric public transport fare zones in the Île-de-France region surrounding central Paris.
-
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 2
Zone 2 is a fare zone within a public transit system used to determine ticket prices and travel boundaries.
-
E.
Zone 1
Zone 1 is the central London public transport fare zone that covers the city’s main commercial, tourist, and historic areas.
- 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 3 Triple: [London fare zones, includesZone, Zone 3]
Generated description
Zone 3 is a mid-distance public transport fare zone in London covering various suburban residential and commercial areas outside the city center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zone 3 Target entity description: Zone 3 is a mid-distance public transport fare zone in London covering various suburban residential and commercial areas outside the city center.
-
A.
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.
-
B.
Zone 3
Zone 3 is one of the concentric public transport fare zones in the Île-de-France region surrounding central Paris.
-
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 2
Zone 2 is a fare zone within a public transit system used to determine ticket prices and travel boundaries.
-
E.
Zone 1
Zone 1 is the central London public transport fare zone that covers the city’s main commercial, tourist, and historic areas.
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f03d3c88190baedffb83465bff8 |
completed | April 10, 2026, 6:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a9d50b081908f0bdb7a2ca2832a |
completed | May 2, 2026, 4:47 p.m. |
| NEDg | Description generation | batch_69f62be420308190bcb00d8b37b09ea2 |
completed | May 2, 2026, 4:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f63050f5d48190881688d12c4c1819 |
completed | May 2, 2026, 5:11 p.m. |
Created at: April 8, 2026, 9:53 p.m.