Triple
T2790859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daphne Koller |
E61924
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Calico Labs |
E35320
|
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: Calico Labs | Statement: [Daphne Koller, employer, Calico Labs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calico Labs Context triple: [Daphne Koller, employer, Calico Labs]
-
A.
Calico LLC
chosen
Calico LLC is a research and development company focused on understanding aging and developing technologies to extend human healthspan, backed by Alphabet Inc.
-
B.
Habana Labs
Habana Labs is an Israeli-based company specializing in artificial intelligence accelerators and deep learning processors for data centers.
-
C.
Kapor Capital
Kapor Capital is an early-stage venture capital firm focused on investing in technology-driven startups that aim to close gaps in access, opportunity, and outcomes for underserved communities.
-
D.
Pyra Labs
Pyra Labs is the software company best known for creating Blogger, one of the earliest and most influential web-based blogging platforms.
-
E.
Founders Lab
Founders Lab is an innovation and entrepreneurship space at Elmhurst University that supports student startups and experiential learning in business and technology.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddcfe19c81908f94dd449f18a2c2 |
completed | March 7, 2026, 8:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc65c1f848190b6efeefb64a3e131 |
completed | March 10, 2026, 7:21 a.m. |
Created at: March 6, 2026, 9:58 p.m.