Triple
T11131031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theresa Ann Lane |
E263280
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Theresa Ann Lane |
E263280
|
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: Theresa Ann Lane | Statement: [Theresa Ann Lane, name, Theresa Ann Lane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Theresa Ann Lane Context triple: [Theresa Ann Lane, name, Theresa Ann Lane]
-
A.
Theresa Ann Lane
chosen
Theresa Ann Lane is best known as the wife of legendary American country music singer and songwriter Merle Haggard.
-
B.
Theresa Ann Walker
Theresa Ann Walker is best known as the wife of prominent civil rights leader and Martin Luther King Jr. aide Wyatt Tee Walker.
-
C.
Theresa Galloway
Theresa Galloway is known as the wife of American war correspondent and author Joseph L. Galloway.
-
D.
Theresa Randle
Theresa Randle is an American actress known for her roles in films such as "Bad Boys," "Spawn," and "Girl 6."
-
E.
Theresa Russell
Theresa Russell is an American actress known for her intense and often provocative performances in films such as "Bad Timing," "Black Widow," and "Track 29."
- 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_69d6aa9c0ba08190bbd19c217489b755 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e831f4808190afabdaa0e97bbe32 |
completed | April 9, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e441e6b72881908f8288e99df0cb7c |
completed | April 19, 2026, 2:45 a.m. |
Created at: April 8, 2026, 9:28 p.m.