Triple
T18794404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject |
E459595
|
entity | |
| Predicate | formerName |
P65
|
FINISHED |
| Object | OICQ |
—
|
NE NERFINISHED |
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: OICQ | Statement: [QQ, formerName, OICQ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OICQ Context triple: [QQ, formerName, OICQ]
-
A.
ICQ
chosen
ICQ is one of the earliest popular internet instant messaging services, widely used in the late 1990s and early 2000s.
-
B.
AOL Instant Messenger
AOL Instant Messenger was a pioneering late-1990s and early-2000s instant messaging service that popularized online chat and status-based communication for mainstream internet users.
-
C.
IQQ
IQQ is the IATA airport code for Diego Aracena International Airport, which serves the city of Iquique in northern Chile.
-
D.
QQP
QQP is the National Rail station code used to identify London Paddington railway station in the United Kingdom.
-
E.
Yahoo! Messenger
Yahoo! Messenger was a popular instant messaging client and service from Yahoo that enabled real-time text, voice, and video communication, widely used in the late 1990s and 2000s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a01cc0c0819098ef4326e82ff524 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.