Triple
T23950198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | highlight shows like ESPN SportsCenter gained importance |
E603024
|
entity |
| Predicate | effectOnFans |
P53532
|
FINISHED |
| Object | shaped how fans consumed NBA action |
—
|
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: shaped how fans consumed NBA action | Statement: [highlight shows like ESPN SportsCenter gained importance, effectOnFans, shaped how fans consumed NBA action]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnFans Context triple: [highlight shows like ESPN SportsCenter gained importance, effectOnFans, shaped how fans consumed NBA action]
-
A.
effectOnViewers
chosen
Indicates the impact or influence that something has on those who observe or experience it.
-
B.
effectOnOthers
Indicates the impact or influence that one entity’s actions, presence, or state has on other entities.
-
C.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
D.
audienceImpact
Indicates how an action, message, or event affects, influences, or resonates with its intended audience.
-
E.
fanInvolved
Indicates that a fan actively participates in or is directly involved with a particular event, activity, or interaction.
- 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_69e2953e4924819093f1c24c03476b42 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d03140f08190b4356626628ff56f |
completed | April 29, 2026, 9:32 a.m. |
| PD | Predicate disambiguation | batch_69f1615518088190a206f54e2fdb14a3 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:19 p.m.