Triple
T16850192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alphabet Other Bets |
E409654
|
entity |
| Predicate | includesBusiness |
P110112
|
FINISHED |
| Object | Verily |
E184233
|
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: Verily | Statement: [Alphabet Other Bets, includesBusiness, Verily]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verily Context triple: [Alphabet Other Bets, includesBusiness, Verily]
-
A.
Verily
chosen
Verily is a life sciences and healthcare technology company under Alphabet Inc. that focuses on using data and advanced tools to improve health outcomes.
-
B.
Veritas
Veritas is the Latin word for "truth" and is famously used as the motto of Harvard University.
-
C.
Celestine
Celestine is a feminine given name of Latin origin, derived from "caelestis" meaning "heavenly" or "celestial."
-
D.
Signius
Signius is the given first name of William S. Knudsen, a prominent Danish-American industrialist and automotive executive who played a key role in U.S. wartime production during World War II.
-
E.
Talpiot
Talpiot is a neighborhood in southern Jerusalem known for its mix of residential areas, light industry, and commercial centers.
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b378dda48190ab81d75f2cfe3ab3 |
completed | April 18, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb1f02648190937c692af83843dc |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 5:24 a.m.