Triple
T11169368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryan Smith |
E264234
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Qualtrics |
E908816
|
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: Qualtrics | Statement: [Ryan Smith, employer, Qualtrics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Qualtrics Context triple: [Ryan Smith, employer, Qualtrics]
-
A.
Qualtrics
chosen
Qualtrics is an experience management and survey software company known for helping organizations collect and analyze customer, employee, product, and brand feedback.
-
B.
SurveyMonkey
SurveyMonkey is a leading online survey and experience management platform that enables individuals and organizations to create, distribute, and analyze surveys and feedback data.
-
C.
Crowdsignal
Crowdsignal is an online survey and polling platform, best known for enabling users to easily create and distribute questionnaires and collect feedback on the web.
-
D.
Microsoft Forms
Microsoft Forms is a web-based application from Microsoft that lets users easily create surveys, quizzes, and polls and collect responses in real time.
-
E.
Gainsight
Gainsight is a customer success and product experience software company known for helping businesses reduce churn, drive expansion, and improve customer retention through data-driven insights and workflows.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8952e248190b0751669e8c960b7 |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e483816af08190877f86ee52846581 |
completed | April 19, 2026, 7:25 a.m. |
Created at: April 8, 2026, 9:29 p.m.