Triple
T17069471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macaulay Culkin |
E414174
|
entity |
| Predicate | partner |
P1136
|
FINISHED |
| Object | Brenda Song |
E244220
|
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: Brenda Song | Statement: [Macaulay Culkin, partner, Brenda Song]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brenda Song Context triple: [Macaulay Culkin, partner, Brenda Song]
-
A.
Brenda Song
chosen
Brenda Song is an American actress known for her roles in Disney Channel productions like "The Suite Life of Zack & Cody" and in acclaimed films such as "The Social Network."
-
B.
Laura Harrier
Laura Harrier is an American actress and model best known for her roles in films like "Spider-Man: Homecoming" and "BlacKkKlansman."
-
C.
Jessica Lu
Jessica Lu is an American actress best known for her television roles, including a main role on the sci-fi drama series "Reverie."
-
D.
Julia Hsu
Julia Hsu is an actress best known for her role as Soo-Yung, the kidnapped daughter of a Chinese consul, in the action-comedy film "Rush Hour."
-
E.
Brianne Tju
Brianne Tju is an American actress known for her roles in teen and horror television series and films, including the thriller "47 Meters Down: Uncaged."
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbbfb1f08190807301ff6e573cf5 |
completed | April 18, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01235278d48190bacc7881b9eb1ea1 |
completed | May 11, 2026, 12:31 a.m. |
Created at: April 10, 2026, 5:34 a.m.