Triple
T8936097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Love Hewitt |
E212778
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | CBS |
E6070
|
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: CBS | Statement: [Jennifer Love Hewitt, employer, CBS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CBS Context triple: [Jennifer Love Hewitt, employer, CBS]
-
A.
CBS
CBS is a leading Danish university in Copenhagen specializing in business and economics education and research.
-
B.
CBS
CBS is the National Rail station code assigned to Coatbridge Sunnyside railway station in North Lanarkshire, Scotland.
-
C.
CBS
CBS is a leading graduate business school of Columbia University in New York City, renowned for its MBA and finance programs.
-
D.
CBS
CBS is the commonly used abbreviation for the Commission for Basic Systems, a specialized body focused on foundational infrastructure and standards, likely within an international or governmental organizational context.
-
E.
CBS
chosen
CBS is a major American broadcast television network known for airing a wide range of popular news, sports, and entertainment programming nationwide.
- 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_69ca839694c88190b324ffeb43d23b08 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66928cec8190915636d663f843af |
completed | April 1, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc937ded08190a67fd4457b6458ff |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:58 p.m.