Triple
T12314520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London fare zones |
E293566
|
entity |
| Predicate | includesZone |
P6793
|
FINISHED |
| Object | Zone 5 |
E283354
|
NE FINISHED |
How this triple was built (2 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 5 | Statement: [London fare zones, includesZone, Zone 5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zone 5 Context triple: [London fare zones, includesZone, Zone 5]
-
A.
Zone 5
chosen
Zone 5 is an outer fare zone in the London public transport system used for calculating ticket and Travelcard prices.
-
B.
Zone 4
Zone 4 is a suburban travel zone in London’s public transport fare system, covering outer residential areas served by the Underground, Overground, and National Rail services.
-
C.
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.
-
D.
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.
-
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.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69f63eefad508190be266c776525a7cc |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:53 p.m.