Triple
T3742079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arlington Trolley (event service) |
E79723
|
entity |
| Predicate | targetEventType |
P9903
|
FINISHED |
| Object | professional football games |
—
|
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: professional football games | Statement: [Arlington Trolley (event service), targetEventType, professional football games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetEventType Context triple: [Arlington Trolley (event service), targetEventType, professional football games]
-
A.
associatedEventType
Indicates that one entity is linked to another by the type or category of event with which it is associated.
-
B.
hasEventType
Indicates that an event is associated with, or classified under, a specific type or category of event.
-
C.
primaryTargetType
chosen
Indicates the main category or type of entity that is the principal focus or intended recipient of an action, effect, or operation.
-
D.
typicalEvent
Indicates that the associated event is a common, characteristic, or prototypical occurrence for the given entity or situation.
-
E.
supportedEvent
Indicates that an entity is capable of recognizing, handling, or being associated with a particular type of event.
- 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_69ad8b115610819095b02007da5ca3cb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb563fac8190b6a55580d0386e8a |
completed | March 8, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69adc048f28c819092bed16a95a3cac1 |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.