Triple
T20221280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple WWDC 2018 |
E495259
|
entity |
| Predicate | hasKeynoteSpeaker |
P9374
|
FINISHED |
| Object | Eddy Cue |
—
|
NE NERFINISHED |
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: Eddy Cue | Statement: [Apple WWDC 2018, hasKeynoteSpeaker, Eddy Cue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eddy Cue Context triple: [Apple WWDC 2018, hasKeynoteSpeaker, Eddy Cue]
-
A.
Eddy Cue
chosen
Eddy Cue is a senior Apple executive best known for overseeing the company’s internet software and services, including iTunes, the App Store, and iCloud.
-
B.
Mark Hurd (former)
Mark Hurd was an American technology executive best known for serving as CEO of Hewlett-Packard and later as co-CEO of Oracle Corporation.
-
C.
John Chambers
John Chambers was an acclaimed American makeup artist best known for his groundbreaking prosthetic work on films like "Planet of the Apes," which earned him a special Academy Award.
-
D.
John Chambers
John Chambers is an American business executive best known for serving as the longtime CEO and chairman of Cisco Systems.
-
E.
John Chambers
John Chambers is a statistician and computer scientist best known for creating the S programming language, a precursor to R, while at Bell Labs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626cff80819097b530718a7c98b6 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66fd610f881908fdd22b1f8bd2efc |
completed | April 20, 2026, 6:26 p.m. |
Created at: April 11, 2026, 11:39 p.m.