Triple
T3374739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurence Tisch |
E71039
|
entity |
| Predicate | business |
P12567
|
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: [Laurence Tisch, business, CBS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CBS Context triple: [Laurence Tisch, business, CBS]
-
A.
CBS
CBS is a leading Danish university in Copenhagen specializing in business and economics education and research.
-
B.
CBS
CBS is a leading graduate business school of Columbia University in New York City, renowned for its MBA and finance programs.
-
C.
CBS
chosen
CBS is a major American broadcast television network known for airing a wide range of popular news, sports, and entertainment programming nationwide.
-
D.
NBC
NBC is a major American broadcast television network known for its nationwide programming, including news, sports, and entertainment.
-
E.
Columbia Broadcasting System
Columbia Broadcasting System (CBS) is a major American commercial broadcast television and radio network known for its influential news, entertainment, and sports programming.
- 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_69ad85a7f80c8190a05e43013f298942 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2e5fc6c81909ff582611751096d |
completed | March 8, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b35460b4a081908f05bf786cc9bcd3 |
completed | March 13, 2026, 12:03 a.m. |
Created at: March 8, 2026, 3:13 p.m.