Triple
T4386803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sean Parker |
E99262
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Plaxo |
E435077
|
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: Plaxo | Statement: [Sean Parker, employer, Plaxo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Plaxo Context triple: [Sean Parker, employer, Plaxo]
-
A.
Plaxo
chosen
Plaxo was an online address book and social networking service that helped users manage and synchronize their contact information across multiple platforms.
-
B.
Genesys
Genesys is a global customer experience and contact center technology company known for its cloud-based solutions that help businesses manage and optimize customer interactions.
-
C.
Intuit
Intuit is an American financial software company best known for products like TurboTax, QuickBooks, and Mint that help individuals and small businesses manage taxes, accounting, and personal finance.
-
D.
Ultimate Software
Ultimate Software was a leading American provider of cloud-based human capital management and payroll software solutions for businesses.
-
E.
Namesys
Namesys was a software company best known for developing the ReiserFS journaling file system for Linux.
- 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352669f608190b3aa7030d8073e04 |
completed | March 12, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f5e74ba481908876629c811d934f |
completed | March 14, 2026, 11:57 p.m. |
Created at: March 12, 2026, 11:19 p.m.