Triple
T894597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold Macmillan |
E19315
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Maurice |
E44841
|
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: Maurice | Statement: [Harold Macmillan, givenName, Maurice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maurice Context triple: [Harold Macmillan, givenName, Maurice]
-
A.
Maurice
chosen
Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
-
B.
Bernard
Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
-
C.
Georges
Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
-
D.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
E.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
- 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_69a4939d37188190848be3d426ebc9ae |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad22b6fc819093e655c8ce1f738b |
completed | March 1, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac1cd167188190aa05992376553550 |
completed | March 7, 2026, 12:40 p.m. |
Created at: March 1, 2026, 7:39 p.m.