Triple
T270088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maurice Pryce |
E5612
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Maurice
Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
|
E44841
|
NE FINISHED |
How this triple was built (4 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: [Maurice Pryce, givenName, Maurice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maurice Context triple: [Maurice Pryce, givenName, Maurice]
-
A.
Bernard
Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
-
B.
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."
-
C.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
D.
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."
-
E.
Herbert
Herbert is a masculine given name of Germanic origin that has been borne by various notable figures, including U.S. President Herbert Hoover.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Maurice Triple: [Maurice Pryce, givenName, Maurice]
Generated description
Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maurice Target entity description: Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
-
A.
Bernard
Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
-
B.
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."
-
C.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
D.
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."
-
E.
Herbert
Herbert is a masculine given name of Germanic origin that has been borne by various notable figures, including U.S. President Herbert Hoover.
- F. None of above. chosen
Provenance (5 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25db17e8c8190a6ebcadd8a0abf4d |
completed | Feb. 28, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3dd2a946881909c7a53f3c712e0e3 |
completed | March 1, 2026, 6:31 a.m. |
| NEDg | Description generation | batch_69a3de184c4c8190a9d6c21967618efc |
completed | March 1, 2026, 6:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3df0cc8708190991731415d28d96b |
completed | March 1, 2026, 6:39 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.