Triple
T2567995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft Mobile |
E57595
|
entity |
| Predicate | keyPerson |
P256
|
FINISHED |
| Object |
Jo Harlow
Jo Harlow is a technology executive best known for leading mobile device and smartphone businesses at companies such as Nokia and later Microsoft.
|
E294352
|
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: Jo Harlow | Statement: [Microsoft Mobile, keyPerson, Jo Harlow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jo Harlow Context triple: [Microsoft Mobile, keyPerson, Jo Harlow]
-
A.
Volga Hayworth
Volga Hayworth was the mother of Hollywood actress Rita Hayworth and part of the family background that shaped the star's early life.
-
B.
Vina Wray
Vina Wray is an alternate name for Fay Wray, the Canadian-American actress best known for her iconic role in the 1933 film "King Kong."
-
C.
Maxene Reynolds
Maxene Reynolds is the daughter of legendary American actress and singer Debbie Reynolds.
-
D.
Molly Blane
Molly Blane is a key member of the covert military team in the television series "The Unit," known for her resilience and role within the soldiers’ family network.
-
E.
Farrah Franklin
Farrah Franklin is an American singer and actress best known for her brief tenure as a member of the R&B girl group Destiny's Child in 2000.
- 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: Jo Harlow Triple: [Microsoft Mobile, keyPerson, Jo Harlow]
Generated description
Jo Harlow is a technology executive best known for leading mobile device and smartphone businesses at companies such as Nokia and later Microsoft.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jo Harlow Target entity description: Jo Harlow is a technology executive best known for leading mobile device and smartphone businesses at companies such as Nokia and later Microsoft.
-
A.
Volga Hayworth
Volga Hayworth was the mother of Hollywood actress Rita Hayworth and part of the family background that shaped the star's early life.
-
B.
Vina Wray
Vina Wray is an alternate name for Fay Wray, the Canadian-American actress best known for her iconic role in the 1933 film "King Kong."
-
C.
Maxene Reynolds
Maxene Reynolds is the daughter of legendary American actress and singer Debbie Reynolds.
-
D.
Molly Blane
Molly Blane is a key member of the covert military team in the television series "The Unit," known for her resilience and role within the soldiers’ family network.
-
E.
Farrah Franklin
Farrah Franklin is an American singer and actress best known for her brief tenure as a member of the R&B girl group Destiny's Child in 2000.
- 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_69ab4a51410081908501dcf8bad9adc4 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd36191848190b6255fa9029429bd |
completed | March 7, 2026, 7:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbaef55881909ef223f366c209c7 |
completed | March 10, 2026, 6:35 a.m. |
| NEDg | Description generation | batch_69afbc545dc48190b61ed2ee73d53737 |
completed | March 10, 2026, 6:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afbcd39c608190b01924370208f1ec |
completed | March 10, 2026, 6:40 a.m. |
Created at: March 6, 2026, 9:48 p.m.