Triple
T6248446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shirley Hufstedler |
E139982
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Shirley |
E7949
|
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: Shirley | Statement: [Shirley Hufstedler, givenName, Shirley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shirley Context triple: [Shirley Hufstedler, givenName, Shirley]
-
A.
Shirley
Shirley is a small town in north-central Massachusetts served by commuter rail on the MBTA Fitchburg Line.
-
B.
Shirley
chosen
Shirley is the given name of Shirley Ann Jackson, a prominent American physicist and trailblazing academic leader.
-
C.
Shirley
Shirley is an English surname of Old English origin that has also become a common given name.
-
D.
Shirley
"Shirley" is a social and political novel by Charlotte Brontë set during the industrial unrest of early 19th-century England, exploring themes of class conflict, gender roles, and economic hardship.
-
E.
Shirley
Shirley is a suburban area in the London Borough of Croydon, known for its residential neighborhoods and proximity to green spaces and nearby districts like West Wickham.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0633a9a048190856d5247d3b28a2e |
completed | March 22, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5191d2d248190b34edca246ee74b8 |
completed | March 26, 2026, 11:31 a.m. |
Created at: March 22, 2026, 4:24 p.m.