Triple
T20079578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lionel Messi Inter Miami CF home debut |
E499961
|
entity |
| Predicate | impactOnTicketDemand |
P27056
|
FINISHED |
| Object | significant increase |
—
|
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: significant increase | Statement: [Lionel Messi Inter Miami CF home debut, impactOnTicketDemand, significant increase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnTicketDemand Context triple: [Lionel Messi Inter Miami CF home debut, impactOnTicketDemand, significant increase]
-
A.
impactOnMarket
Indicates the effect or influence that one factor, event, or action has on market conditions, behavior, or outcomes.
-
B.
ticketDemand
chosen
Indicates that there is a level of desire or need among potential buyers for tickets to an event, service, or offering.
-
C.
impactStatus
Indicates the current state or condition of how something has affected or influenced a target.
-
D.
impactOnIssuance
Indicates the effect that one factor, event, or condition has on whether, how, or to what extent something is issued.
-
E.
impactOnBusiness
Indicates the effect or influence that one factor, event, or action has on a business’s performance, operations, or outcomes.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6643f93208190ae2a413f88ea9aed |
completed | April 20, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69e54cf369b88190931532420517dac7 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:40 p.m.