Triple
T16684197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucy Watson |
E405415
|
entity |
| Predicate | socialMediaPresence |
P57
|
FINISHED |
| Object |
E3345
|
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: Twitter | Statement: [Lucy Watson, socialMediaPresence, Twitter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Twitter Context triple: [Lucy Watson, socialMediaPresence, Twitter]
-
A.
Tweet
The Tweet is the nickname of the Cessna T-37, a small twin‑engine jet trainer aircraft used extensively by the U.S. Air Force and several other countries for pilot training.
-
B.
Tweeter
Tweeter is a fictional character from the Traveling Wilburys’ song “Tweeter and the Monkey Man,” depicted as a small-time criminal entangled in a noir-style tale of crime and betrayal.
-
C.
Twitter, Inc.
chosen
Twitter, Inc. was a major social media and microblogging company best known for its real-time short-message platform that shaped online news, politics, and public discourse worldwide.
-
D.
Weibo
Weibo is a major Chinese microblogging and social media platform widely used for news, entertainment, and public discourse.
-
E.
Instagram
Instagram is a popular photo and video sharing social media platform known for its visual content, stories, and influencer culture.
- 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_69d8838c28748190b3f5967c743940ab |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37d71a66881908c8d06cc074fdf29 |
completed | April 18, 2026, 12:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a422f8c8190873fd7089df8fbd4 |
completed | May 10, 2026, 1:38 p.m. |
Created at: April 10, 2026, 5:19 a.m.