Triple
T10600686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeanette Kittredge Watson |
E275737
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Watson |
E91959
|
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: Watson | Statement: [Jeanette Kittredge Watson, familyName, Watson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Watson Context triple: [Jeanette Kittredge Watson, familyName, Watson]
-
A.
Watson
chosen
Watson is a common English surname borne by numerous notable figures, including scientists, artists, and public personalities.
-
B.
Watson
Watson is a residential suburb in Canberra, Australia, known for its proximity to natural reserves and easy access to the city’s northeastern bushland.
-
C.
WATSON
WATSON is a close-up imaging camera on NASA’s Perseverance rover used to examine the fine details of Martian rocks and surface materials.
-
D.
IBM Watson
IBM Watson is IBM’s artificial intelligence platform known for its natural language processing, machine learning capabilities, and high-profile applications such as winning on Jeopardy! and powering enterprise AI solutions.
-
E.
Bixby
Bixby is Samsung's proprietary virtual assistant designed to enable voice control, smart device integration, and contextual assistance across the company's ecosystem of products.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6ded52f288190b40288d0acbe009b |
completed | April 8, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95ea8f1688190aa36e29b52667d26 |
completed | April 10, 2026, 8:33 p.m. |
Created at: April 8, 2026, 7:31 p.m.