Triple
T21039286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lawrence Tierney |
E518275
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lawrence |
—
|
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: Lawrence | Statement: [Lawrence Tierney, givenName, Lawrence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lawrence Context triple: [Lawrence Tierney, givenName, Lawrence]
-
A.
Lawrence
Lawrence is an American soul-pop band known for its energetic live performances, tight musicianship, and catchy, horn-driven songs.
-
B.
Lawrence
Lawrence is a college town in northeastern Kansas best known as the home of the University of Kansas and its athletic programs.
-
C.
Lawrence
Lawrence is a small rural village in the Clarence Valley region of New South Wales, Australia, known for its historic charm and riverside setting on the Clarence River.
-
D.
Lawrence
chosen
Lawrence is a common English surname of Norman origin, derived from the given name Laurence and historically associated with various notable families and individuals.
-
E.
Lawrence
Lawrence is the middle-aged civil servant protagonist of the British television film "The Girl in the Café," whose chance meeting with a young woman profoundly affects his personal life and political conscience.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcee13b08190a8b3372f6759cd1b |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:14 p.m.