Triple
T20086523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lindsey |
E496145
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Lindsy |
—
|
NE NERFINISHED |
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: Lindsy | Statement: [Lindsey, hasVariant, Lindsy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lindsy Context triple: [Lindsey, hasVariant, Lindsy]
-
A.
Lindsey
Lindsey is a historic region in eastern England, traditionally forming the northern part of the county of Lincolnshire.
-
B.
Lindsey
chosen
Lindsey is a given name commonly used in English-speaking countries for both females and males.
-
C.
Lyndie
Lyndie is a Canadian actress best known for her role as Jenny Mills on the television series "Sleepy Hollow."
-
D.
Lindsay
Lindsay is an Australian federal electoral division in New South Wales that includes the city of Penrith and surrounding suburbs in Western Sydney.
-
E.
Lindsay
Lindsay is a consumer brand best known for its olives and related Mediterranean-style food products.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6655ba40c8190adea0e271a1249cf |
completed | April 20, 2026, 5:41 p.m. |
Created at: April 11, 2026, 11:04 p.m.