Triple
T20183690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Laurence |
E492797
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Laurence |
—
|
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: Laurence | Statement: [Mr. Laurence, hasSurname, Laurence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laurence Context triple: [Mr. Laurence, hasSurname, Laurence]
-
A.
Laurence
chosen
Laurence is a masculine given name of Latin origin, commonly used in English-speaking countries.
-
B.
Laurence
Laurence is one of the central characters in Mike Leigh’s play and film "Abigail’s Party," typically portrayed as a tense, status-conscious suburban husband.
-
C.
Laurence Dominic
Laurence Dominic is a character from the science fiction television series "Dollhouse," serving as the head of security for the Dollhouse organization.
-
D.
Theodore "Laurie" Laurence
Theodore "Laurie" Laurence is a charming, wealthy young man and close friend of the March family in Louisa May Alcott's novel "Little Women," who ultimately marries Amy March.
-
E.
Laurence Boone
Laurence Boone is a French economist and diplomat who serves as France’s ambassador to the United States.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e668f068748190a0941e98ef5afd59 |
completed | April 20, 2026, 5:57 p.m. |
Created at: April 11, 2026, 11:36 p.m.