Triple
T4197477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olivia Thirlby |
E85987
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Thirlby |
E85987
|
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: Thirlby | Statement: [Olivia Thirlby, hasSurname, Thirlby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thirlby Context triple: [Olivia Thirlby, hasSurname, Thirlby]
-
A.
Thirlby
chosen
Thirlby is the surname of Olivia Thirlby, an American actress known for roles in films such as "Juno" and "Dredd."
-
B.
Hartley
Hartley is an English-language surname of Old English origin, commonly associated with various notable figures across fields such as science, politics, and the arts.
-
C.
Hartley
Hartley is a small census-designated community located in Solano County, California.
-
D.
Lindley
Lindley refers to John Lindley, a prominent 19th-century English botanist known for his influential work in plant taxonomy and classification.
-
E.
Yates
Yates is a surname of English origin borne by various notable individuals across literature, politics, sports, and other fields.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0360bc8081908ceb2483eef89174 |
completed | March 9, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b58a0ff2a08190ac7f89d306454ab8 |
completed | March 14, 2026, 4:17 p.m. |
Created at: March 9, 2026, 3:48 p.m.