Triple
T8381692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Connie Chung |
E197706
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | CNN |
E10223
|
NE 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: CNN | Statement: [Connie Chung, employer, CNN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CNN Context triple: [Connie Chung, employer, CNN]
-
A.
CNN
chosen
CNN is a major American cable news television channel known for pioneering 24-hour news coverage and live reporting from global events.
-
B.
NBC News Now
NBC News Now is a free, ad-supported streaming news channel from NBC News that provides live, rolling coverage and original news programming across digital platforms.
-
C.
NBC News
NBC News is a major American television news division known for producing national and international news programs across broadcast and digital platforms.
-
D.
CNN2
CNN2 was the original name of HLN, a U.S. cable news channel that focused on headline news and brief, continuously updated reports.
-
E.
ESPN News
ESPN News is a 24-hour American sports news television channel providing continuous coverage, highlights, and analysis of major sporting events.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82f64c188190af4e1608036b865d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80dc96048190887d7df8bce5c1fd |
completed | March 31, 2026, 8:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cde814816481909bcc3b11fa5a1367 |
completed | April 2, 2026, 3:52 a.m. |
Created at: March 30, 2026, 6:02 p.m.