Triple
T13185687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Caine |
E313841
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Natasha Caine |
E251238
|
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: Natasha Caine | Statement: [Michael Caine, hasChild, Natasha Caine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Natasha Caine Context triple: [Michael Caine, hasChild, Natasha Caine]
-
A.
Natasha Caine
chosen
Natasha Caine is a British personality best known as the daughter of acclaimed actor Sir Michael Caine.
-
B.
Chelsea Finn
Chelsea Finn is a prominent computer scientist and roboticist known for her influential research in meta-learning, reinforcement learning, and generalizable robot learning.
-
C.
Samantha Caine
Samantha Caine is the amnesiac suburban schoolteacher who gradually uncovers her past as a lethal government assassin in the action thriller "The Long Kiss Goodnight."
-
D.
Rachel Kane
Rachel Kane is a key CIA operative and mission handler in the video game Call of Duty: Black Ops III, guiding and assisting the player throughout much of the campaign.
-
E.
Grace Hawkins
Grace Hawkins is the seemingly mild-mannered yet secretly murderous housekeeper at the center of the dark comedy film "Keeping Mum."
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c4b663c8190b0b18f0785f7b57d |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716c1080c81908057f92f320a855b |
completed | May 3, 2026, 9:34 a.m. |
Created at: April 9, 2026, 9:15 p.m.