Triple
T394297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurence K. Marshall |
E8946
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Laurence |
E48169
|
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: Laurence | Statement: [Laurence K. Marshall, hasGivenName, Laurence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laurence Context triple: [Laurence K. Marshall, hasGivenName, Laurence]
-
A.
Laurence
chosen
Laurence is a masculine given name of Latin origin, commonly used in English-speaking countries.
-
B.
Leonard
Leonard is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by numerous notable figures in arts, sports, and public life.
-
C.
Harold
Harold is a masculine given name of Old English origin, historically borne by several notable figures including kings and modern public personalities.
-
D.
Bernard
Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
-
E.
Walter
Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec77a8a08190b6f96373aa8c1346 |
completed | Feb. 28, 2026, 1:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a467f9e87481909e59ee41f558689c |
completed | March 1, 2026, 4:23 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.