Triple
T1464905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guy Carleton |
E26999
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Guy |
E66577
|
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: Guy | Statement: [Guy Carleton, givenName, Guy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guy Context triple: [Guy Carleton, givenName, Guy]
-
A.
Guy
chosen
Guy is a masculine given name of French origin that has been widely used in English-speaking countries.
-
B.
Gary
Gary is an industrial city in northwest Indiana, historically known for its steel production and location within the American Rust Belt.
-
C.
Greg
Greg is a male given name commonly used in English-speaking countries, often as a short form of Gregory.
-
D.
Ger
Ger is a prominent Hasidic dynasty, originating in Góra Kalwaria, Poland, known for its large following and significant influence within the Haredi Jewish world.
-
E.
Gus
Gus is a character from T. S. Eliot's "Old Possum's Book of Practical Cats," depicted as an elderly, once-famous theater cat reflecting nostalgically on his past glory.
- 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_69a496d25d6881909dbd84f86d763992 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c5ba2d5c81909ee85713de961fcb |
completed | March 1, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad0e7c8a608190b3ca574c118e89b5 |
completed | March 8, 2026, 5:51 a.m. |
Created at: March 1, 2026, 8:01 p.m.