Triple
T10786369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode 7.0 |
E254460
|
entity |
| Predicate | addsBlock |
P62553
|
FINISHED |
| Object | Emoticons |
E453718
|
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: Emoticons | Statement: [Unicode 7.0, addsBlock, Emoticons]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emoticons Context triple: [Unicode 7.0, addsBlock, Emoticons]
-
A.
Emoticons block
chosen
The Emoticons block is a range of Unicode characters dedicated to standardized pictorial faces and expressions used to convey emotions in digital text.
-
B.
Animoji and Memoji
Animoji and Memoji are Apple’s animated, customizable characters that mirror a user’s facial expressions and are used in messaging and video calls on compatible Apple devices.
-
C.
Smiley Face
Smiley Face is a 2007 stoner comedy film starring Anna Faris as an out-of-work actress who embarks on a chaotic, marijuana-fueled day in Los Angeles.
-
D.
Bitmoji
Bitmoji is a popular app and feature that lets users create personalized cartoon avatars and use them as expressive stickers across messaging and social platforms.
-
E.
Smiles
Smiles is a Brazilian frequent-flyer and loyalty program that allows members to earn and redeem miles across flights, partner airlines, and various retail and service partners.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732d5422481908d7ab833c6cbc879 |
completed | April 9, 2026, 5:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de56199d088190938a72105540cf66 |
completed | April 14, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:17 p.m.