Triple
T8572037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Tilley |
E202948
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Elizabeth |
E635861
|
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: Elizabeth | Statement: [Elizabeth Tilley, givenName, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Elizabeth Tilley, givenName, Elizabeth]
-
A.
Elizabeth
Elizabeth is the middle name of Lady Sarah Chatto, a British painter and member of the extended royal family.
-
B.
Elizabeth
"Elizabeth" is a popular country and gospel song by The Statler Brothers, known for its rich harmonies and storytelling lyrics.
-
C.
Elizabeth
chosen
Elizabeth is the given first name of American silent film actress Betty Bronson, known for her role as Peter Pan in the 1924 film adaptation.
-
D.
Elizabeth
Elizabeth is the central protagonist of the interactive narrative game "If/Then," around whom the story’s key choices and emotional developments revolve.
-
E.
Elizabeth
Elizabeth is the central character in the Broadway musical "If/Then," a woman who explores how a single choice can lead to radically different life paths.
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea43843c8190ac2224d427bb7a75 |
completed | March 31, 2026, 3:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce8983bd3c819094457b5160bc928d |
completed | April 2, 2026, 3:21 p.m. |
Created at: March 30, 2026, 6:21 p.m.