Triple
T10194518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moonhaven |
E238125
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Emma McDonald |
E850619
|
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: Emma McDonald | Statement: [Moonhaven, castMember, Emma McDonald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma McDonald Context triple: [Moonhaven, castMember, Emma McDonald]
-
A.
Emma McDonald
chosen
Emma McDonald is an actress known for her leading role in the science fiction television series "Moonhaven."
-
B.
Jessica McDonald
Jessica McDonald is an American professional soccer forward known for her prolific scoring in the National Women's Soccer League and contributions to the U.S. women's national team.
-
C.
Samantha MacKenzie
Samantha MacKenzie is the sheltered yet strong-willed daughter of the U.S. President who seeks independence and a normal college life in the romantic comedy film "First Daughter."
-
D.
Mona McKinnon
Mona McKinnon was an American actress best known for her role in Ed Wood’s cult science-fiction film "Plan 9 from Outer Space."
-
E.
Michelle MacLaren
Michelle MacLaren is a Canadian television director and producer best known for her acclaimed work on series like Breaking Bad and Game of Thrones.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc7cc748190bceb8f657afcc054 |
completed | April 2, 2026, 4:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f6e73a2881908563e9e6a02df944 |
completed | April 9, 2026, 12:46 a.m. |
Created at: March 30, 2026, 9:13 p.m.