Triple
T11493525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chafford Hundred railway station |
E272473
|
entity |
| Predicate | hasPassengerUsageGrowth |
P35231
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Chafford Hundred railway station, hasPassengerUsageGrowth, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerUsageGrowth Context triple: [Chafford Hundred railway station, hasPassengerUsageGrowth, yes]
-
A.
hasPassengerUsageStatistics
Indicates the relationship by which an entity is associated with data describing how passengers use it, such as counts, frequencies, or patterns of passenger activity.
-
B.
hasPassengerUsageCategory
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
-
C.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
-
D.
hasHeavyPassengerTraffic
chosen
Indicates that an entity experiences a high volume of passenger movement or usage over a given period.
-
E.
usedForPassengerFlights
Indicates that something serves as a means or facility for transporting passengers on flights.
- F. None of above.
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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85ddffdf88190a00e94ad5b8b91a5 |
completed | April 10, 2026, 2:18 a.m. |
| PD | Predicate disambiguation | batch_69d808736c5c8190899b5b3b2e797f65 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.