Triple
T12319914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Lane |
E293699
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Ella Lane |
E288805
|
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: Ella Lane | Statement: [Sam Lane, relative, Ella Lane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ella Lane Context triple: [Sam Lane, relative, Ella Lane]
-
A.
Ella Lane
chosen
Ella Lane is a supporting character in the Superman/DC Comics universe, best known as the mother of reporter Lois Lane.
-
B.
Dorcas Lane
Dorcas Lane is a central character in the British period drama "Lark Rise to Candleford," known as the capable and warm-hearted postmistress who often guides others in the rural community.
-
C.
Nanny Lane
Nanny Lane is a popular walking track in the Lake District, England, often used as a scenic route for hikers ascending nearby fells.
-
D.
Elizabeth Lane
Elizabeth Lane is the witty, city-dwelling magazine writer who pretends to be a domestic homemaker in the classic holiday film "Christmas in Connecticut."
-
E.
Magpie Lane
Magpie Lane is a narrow historic lane in central Oxford, England, known for its medieval character and proximity to several University of Oxford colleges.
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f4c2b548190938fff9427f07dc7 |
completed | April 10, 2026, 6:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a9f708081908c052333c3b7df4c |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:53 p.m.