Triple
T13902693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russell Tovey |
E334265
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Russell
Russell is a masculine given name of Old French and Anglo-Norman origin, commonly used in English-speaking countries.
|
E368505
|
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: Russell | Statement: [Russell Tovey, givenName, Russell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Russell Context triple: [Russell Tovey, givenName, Russell]
-
A.
Russell
Russell is a rural municipality in eastern Ontario, Canada, known for its bilingual (English and French) community and proximity to Ottawa.
-
B.
Russell
Russell is a locality in Canberra, Australia, known primarily as a major government and defence precinct housing key national security and administrative offices.
-
C.
Russell
Russell is the middle name of Rensselaer Russell Nelson, an American jurist who served as a United States federal judge in the 19th century.
-
D.
Russell
Russell is the enthusiastic young Wilderness Explorer who befriends elderly widower Carl Fredricksen in Pixar's animated film "Up."
-
E.
Russell
Russell is a sharp-tongued, wisecracking young member of the Junkyard Gang in the animated series "Fat Albert and the Cosby Kids."
- 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: Russell Triple: [Russell Tovey, givenName, Russell]
Generated description
Russell is a masculine given name of Old French and Anglo-Norman origin, commonly used in English-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Russell Target entity description: Russell is a masculine given name of Old French and Anglo-Norman origin, commonly used in English-speaking countries.
-
A.
Russell
chosen
Russell is a masculine given name of Old French and Anglo-Norman origin, commonly used in English-speaking countries.
-
B.
Russell
Russell is a prominent English surname historically associated with influential aristocratic and political families in Britain.
-
C.
Russell
Russell is a common English surname most famously associated with legendary Boston Celtics basketball player and civil rights activist Bill Russell.
-
D.
Russell
Russell is the middle name of Rensselaer Russell Nelson, an American jurist who served as a United States federal judge in the 19th century.
-
E.
Russell
Russell is a rural municipality in eastern Ontario, Canada, known for its bilingual (English and French) community and proximity to Ottawa.
- F. None of above.
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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de25d9c7a48190ad8fb0ca676f4f7b |
completed | April 14, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c722e72081909090b2d64000ebd9 |
completed | May 3, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69f7c83a3e04819097b6e0b5a3161b9a |
completed | May 3, 2026, 10:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7c976e96c8190b59f46d9b758e8e2 |
completed | May 3, 2026, 10:17 p.m. |
Created at: April 9, 2026, 10:16 p.m.