Triple
T15037079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal Link train |
E378503
|
entity |
| Predicate | hasNumberOfTerminalsServed |
P2957
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Terminal Link train, hasNumberOfTerminalsServed, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfTerminalsServed Context triple: [Terminal Link train, hasNumberOfTerminalsServed, 2]
-
A.
numberOfTerminals
chosen
Indicates the total count of terminal points or endpoints associated with an entity.
-
B.
hasNumberOfPassengerTerminalsAtAirport
Indicates the relationship that specifies how many passenger terminals are present at a given airport.
-
C.
operatesTerminalsIn
Indicates that an entity manages or runs the operations of one or more terminals at a given location or facility.
-
D.
servesDestinationCount
Indicates the number of distinct destinations that an entity (such as a service, route, or provider) serves.
-
E.
isBusiestTerminalOf
Indicates that one terminal is the busiest (i.e., handles the highest volume of activity) among all terminals associated with a given entity, such as an airport or transportation hub.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded82cf3848190b0b2b6c9e65bc70b |
completed | April 15, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69de9a69d7848190b2b4662dd30f20e9 |
completed | April 14, 2026, 7:50 p.m. |
Created at: April 10, 2026, 2:59 a.m.