Triple
T1041674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oath Inc. |
E22481
|
entity |
| Predicate | brandPortfolio |
P12124
|
FINISHED |
| Object |
Flurry
Flurry is a mobile analytics and advertising platform known for providing app usage insights and monetization tools to developers.
|
E120454
|
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: Flurry | Statement: [Oath Inc., brandPortfolio, Flurry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flurry Context triple: [Oath Inc., brandPortfolio, Flurry]
-
A.
Snowflake
Snowflake is a cloud-based data warehousing platform known for its scalable, high-performance analytics and separation of storage and compute.
-
B.
Clementine
Clementine is a feminine given name most famously borne by Clementine Churchill, the wife of British Prime Minister Winston Churchill.
-
C.
Province 5
Province 5 is one of the administrative provinces of Nepal, located in the western part of the country and known for its diverse geography and cultural heritage.
-
D.
Tikkana
Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
-
E.
Thunder Snow
Thunder Snow is a prominent Irish-bred Thoroughbred racehorse best known for winning back-to-back Dubai World Cups in 2018 and 2019.
- 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: Flurry Triple: [Oath Inc., brandPortfolio, Flurry]
Generated description
Flurry is a mobile analytics and advertising platform known for providing app usage insights and monetization tools to developers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Flurry Target entity description: Flurry is a mobile analytics and advertising platform known for providing app usage insights and monetization tools to developers.
-
A.
Snowflake
Snowflake is a cloud-based data warehousing platform known for its scalable, high-performance analytics and separation of storage and compute.
-
B.
Clementine
Clementine is a feminine given name most famously borne by Clementine Churchill, the wife of British Prime Minister Winston Churchill.
-
C.
Province 5
Province 5 is one of the administrative provinces of Nepal, located in the western part of the country and known for its diverse geography and cultural heritage.
-
D.
Tikkana
Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
-
E.
Thunder Snow
Thunder Snow is a prominent Irish-bred Thoroughbred racehorse best known for winning back-to-back Dubai World Cups in 2018 and 2019.
- 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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bb71e7f88190bf33bbe5ef2c68ff |
completed | March 1, 2026, 10:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bc768948190b1cda4eea93fe4b6 |
completed | March 7, 2026, 2:52 p.m. |
| NEDg | Description generation | batch_69ac3c41ab70819090084c508dbfd295 |
completed | March 7, 2026, 2:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac3cbd43848190854add440753fdad |
completed | March 7, 2026, 2:57 p.m. |
Created at: March 1, 2026, 7:42 p.m.