Triple
T13316980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Control Data Corporation |
E317210
|
entity |
| Predicate | successor |
P78
|
FINISHED |
| Object |
Arbitron
Arbitron was a media and marketing research firm best known for measuring radio audiences in the United States.
|
E208322
|
NE FINISHED |
How this triple was built (4 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: Arbitron | Statement: [Control Data Corporation, successor, Arbitron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arbitron Context triple: [Control Data Corporation, successor, Arbitron]
-
A.
Nielsen Company
Nielsen Company is a global measurement and data analytics firm best known for providing audience and consumer insights across media, entertainment, and retail industries.
-
B.
Nielsen
Nielsen is a common Scandinavian surname, particularly prevalent in Denmark and Norway, traditionally meaning "son of Niels."
-
C.
Nielson
Nielson is a surname and given name that functions as a spelling variant of Nelson, commonly of Scandinavian or English origin.
-
D.
Westwood One
Westwood One is a major American radio network best known for its nationwide sports coverage, including live broadcasts of NFL games.
-
E.
Gallup
Gallup is a small city in northwestern New Mexico known as a historic stop along Route 66 and a cultural center for Native American art and trading.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Arbitron Triple: [Control Data Corporation, successor, Arbitron]
Generated description
Arbitron was a media and marketing research firm best known for measuring radio audiences in the United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arbitron Target entity description: Arbitron was a media and marketing research firm best known for measuring radio audiences in the United States.
-
A.
Nielsen Company
chosen
Nielsen Company is a global measurement and data analytics firm best known for providing audience and consumer insights across media, entertainment, and retail industries.
-
B.
Nielsen
Nielsen is a common Scandinavian surname, particularly prevalent in Denmark and Norway, traditionally meaning "son of Niels."
-
C.
Nielson
Nielson is a surname and given name that functions as a spelling variant of Nelson, commonly of Scandinavian or English origin.
-
D.
Westwood One
Westwood One is a major American radio network best known for its nationwide sports coverage, including live broadcasts of NFL games.
-
E.
Gallup
Gallup is a small city in northwestern New Mexico known as a historic stop along Route 66 and a cultural center for Native American art and trading.
- F. None of above.
Provenance (5 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990f9a384819085890e18255ee339 |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716ee695c81909ffeeb0901ee66c1 |
completed | May 3, 2026, 9:35 a.m. |
| NEDg | Description generation | batch_69f717f4d80c8190a1a95c0f2c83c563 |
completed | May 3, 2026, 9:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f718b852808190a2a0fb48424bffb0 |
completed | May 3, 2026, 9:43 a.m. |
Created at: April 9, 2026, 9:29 p.m.