Triple
T14028929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tinker Bell and the Legend of the NeverBeast |
E337534
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Margaret Hou |
E1196161
|
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: Margaret Hou | Statement: [Tinker Bell and the Legend of the NeverBeast, editedBy, Margaret Hou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margaret Hou Context triple: [Tinker Bell and the Legend of the NeverBeast, editedBy, Margaret Hou]
-
A.
Margaret Hou
chosen
Margaret Hou is a film editor known for her work on the animated superhero movie "Justice League: Doom."
-
B.
Margaret Welsh
Margaret Welsh is an American actress known for her work in film, television, and theater.
-
C.
Margaret Gill
Margaret Gill was the wife of renowned 19th-century African American Shakespearean actor Ira Aldridge.
-
D.
Margaret Chew
Margaret Chew was the wife of American Revolutionary War officer and Maryland statesman John Eager Howard, connected to the prominent Chew and Howard families of the early United States.
-
E.
Margaret Genn
Margaret Genn was the wife of British actor and barrister Leo Genn.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2fa9f8248190930954d609dee5f1 |
completed | April 14, 2026, 12:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0035428e608190b8bb41dabda044d1 |
completed | May 10, 2026, 7:35 a.m. |
Created at: April 9, 2026, 10:20 p.m.