Triple
T16751981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Three Ninjas Kick Back |
E407103
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Martha Chang |
E1224159
|
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: Martha Chang | Statement: [Three Ninjas Kick Back, producer, Martha Chang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martha Chang Context triple: [Three Ninjas Kick Back, producer, Martha Chang]
-
A.
Martha Chang
chosen
Martha Chang is a film producer best known for her work on the family martial arts comedy franchise "Three Ninjas."
-
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.
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."
-
D.
Christina Chang
Christina Chang is a Taiwanese-American actress best known for her role as Dr. Audrey Lim on the medical drama series "The Good Doctor."
-
E.
Margaret Chung
Margaret Chung was a pioneering Chinese American physician and surgeon, widely regarded as the first Chinese American woman doctor in the United States and known for her influential role in supporting U.S. military personnel during World War II.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa282bb08190992c9b61caa7a345 |
completed | April 18, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a52402848190b029cb0be31b4c74 |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:21 a.m.