Triple
T2895282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corpse Bride |
E63924
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Jonathan Lucas |
E210986
|
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: Jonathan Lucas | Statement: [Corpse Bride, editedBy, Jonathan Lucas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Lucas Context triple: [Corpse Bride, editedBy, Jonathan Lucas]
-
A.
Jonathan Lucas
chosen
Jonathan Lucas is a film editor known for his work on the feature film "Troop Zero."
-
B.
Nicholas Lucas
Nicholas Lucas was an early colonial leader known for helping establish the West Jersey settlement in what is now New Jersey.
-
C.
John Paul Lucas
John Paul Lucas is an American architect best known for co-designing the Korean War Veterans Memorial in Washington, D.C.
-
D.
David Luce
David Luce is the child of Elizabeth Root Luce, a member of the Luce family associated with American public service and philanthropy.
-
E.
Jonathan Lisco
Jonathan Lisco is an American television writer, producer, and showrunner known for his work on series such as Animal Kingdom, Halt and Catch Fire, and Jack & Bobby.
- 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_69ab4c45822c8190830c5f2bb97bcfd0 |
completed | March 6, 2026, 9:51 p.m. |
| NER | Named-entity recognition | batch_69abe06509808190b673222b9ae3d599 |
completed | March 7, 2026, 8:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03184d6fc81908119cf090e312c9e |
completed | March 10, 2026, 2:58 p.m. |
Created at: March 6, 2026, 10:08 p.m.