Triple
T1682680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kevin Systrom |
E36371
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Systrom |
E36371
|
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: Systrom | Statement: [Kevin Systrom, familyName, Systrom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Systrom Context triple: [Kevin Systrom, familyName, Systrom]
-
A.
Kevin Systrom
chosen
Kevin Systrom is an American entrepreneur and programmer best known as the co-founder and former CEO of the photo-sharing social media platform Instagram.
-
B.
Evan Spiegel
Evan Spiegel is an American entrepreneur best known as the co-founder and CEO of Snap Inc., the company behind Snapchat.
-
C.
Scott Belsky
Scott Belsky is an American entrepreneur, author, and investor best known as the co-founder of the creative platform Behance and as a longtime product leader at Adobe.
-
D.
Chris Messina
Chris Messina is an American actor known for his versatile roles in film and television, including prominent performances in projects like "The Mindy Project," "Vicky Cristina Barcelona," and "Sharp Objects."
-
E.
David Karp
David Karp is an American web developer and entrepreneur best known for creating the microblogging platform Tumblr.
- 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa627ab38081909d5f264ca49e036a |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad798dbf248190b4f8a8a8a6ee75cf |
completed | March 8, 2026, 1:28 p.m. |
Created at: March 4, 2026, 7:29 p.m.