Triple
T17025445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office (2015 film) |
E413052
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Sylvia Chang |
E1252680
|
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: Sylvia Chang | Statement: [Office (2015 film), stars, Sylvia Chang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sylvia Chang Context triple: [Office (2015 film), stars, Sylvia Chang]
-
A.
Sylvia Chang
chosen
Sylvia Chang is a Taiwanese actress, director, and screenwriter renowned for her influential work in Chinese-language cinema since the 1970s.
-
B.
Fay Chang
Fay Chang is a computer scientist known for co-authoring the influential Google Bigtable paper on large-scale distributed storage systems.
-
C.
Martha Chang
Martha Chang is a film producer best known for her work on the family martial arts comedy franchise "Three Ninjas."
-
D.
Peggy Cherng
Peggy Cherng is a Chinese-American engineer and entrepreneur best known as the co-founder and co-CEO of the Panda Restaurant Group, which operates the Panda Express restaurant chain.
-
E.
Rosalie Chiang
Rosalie Chiang is an American actress best known for voicing the main character, Meilin "Mei" Lee, in Pixar's animated film "Turning Red."
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0148222034819089474594ee351b05 |
completed | May 11, 2026, 3:08 a.m. |
Created at: April 10, 2026, 5:33 a.m.