Triple
T8415928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omnicom Group |
E198729
|
entity |
| Predicate | hasSubsidiary |
P254
|
FINISHED |
| Object |
RAPP
RAPP is a global marketing and customer experience agency known for its data-driven, personalized communications and direct marketing services.
|
E733015
|
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: RAPP | Statement: [Omnicom Group, hasSubsidiary, RAPP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RAPP Context triple: [Omnicom Group, hasSubsidiary, RAPP]
-
A.
Rapp and Rapp
Rapp and Rapp was a prominent early 20th-century American architectural firm best known for designing lavish movie palaces and theaters across the United States.
-
B.
Rappen
Rappen is the German term for the centime-like subunit of the Swiss franc, used to denote its smaller denominations.
-
C.
RAP
RAP is the standard abbreviation used for Raptors 905, the NBA G League affiliate of the Toronto Raptors.
-
D.
Raus
Raus is a German-language surname most notably associated with Erhard Raus, a high-ranking Wehrmacht general during World War II.
-
E.
RANC
RANC is the Royal Australian Navy’s principal officer training institution, responsible for educating and preparing naval officers for service.
- 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: RAPP Triple: [Omnicom Group, hasSubsidiary, RAPP]
Generated description
RAPP is a global marketing and customer experience agency known for its data-driven, personalized communications and direct marketing services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RAPP Target entity description: RAPP is a global marketing and customer experience agency known for its data-driven, personalized communications and direct marketing services.
-
A.
Rapp and Rapp
Rapp and Rapp was a prominent early 20th-century American architectural firm best known for designing lavish movie palaces and theaters across the United States.
-
B.
Rappen
Rappen is the German term for the centime-like subunit of the Swiss franc, used to denote its smaller denominations.
-
C.
RAP
RAP is the standard abbreviation used for Raptors 905, the NBA G League affiliate of the Toronto Raptors.
-
D.
Raus
Raus is a German-language surname most notably associated with Erhard Raus, a high-ranking Wehrmacht general during World War II.
-
E.
RANC
RANC is the Royal Australian Navy’s principal officer training institution, responsible for educating and preparing naval officers for service.
- F. None of above. chosen
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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb84c5121081908efa3eca25406d3a |
completed | March 31, 2026, 8:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0333a3488190ba30d03b1d7bacb1 |
completed | April 2, 2026, 5:48 a.m. |
| NEDg | Description generation | batch_69ce0781859c8190bb92f41c00af459b |
completed | April 2, 2026, 6:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce089d09c08190ba321aed4044a862 |
completed | April 2, 2026, 6:11 a.m. |
Created at: March 30, 2026, 6:06 p.m.