Triple
T146233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PayPal |
E3336
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object | PYPL |
E3336
|
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: PYPL | Statement: [PayPal, tickerSymbol, PYPL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PYPL Context triple: [PayPal, tickerSymbol, PYPL]
-
A.
UPY
UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
-
B.
PayPal
chosen
PayPal is a leading global online payment platform that enables individuals and businesses to send, receive, and manage digital payments securely over the internet.
-
C.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
-
D.
Scratch programming language
Scratch programming language is a visual, block-based coding environment designed primarily for children and beginners to learn programming concepts through creating interactive stories, games, and animations.
-
E.
Udemy
Udemy is a global online learning platform that hosts a vast marketplace of video-based courses across diverse subjects for learners and professionals.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a257ea7eac8190884a53453a9e0dd6 |
completed | Feb. 28, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2c2763ce481908c12046de9003a84 |
completed | Feb. 28, 2026, 10:24 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.