Triple
T13710068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gerardo |
E328746
|
entity |
| Predicate | shortForm |
P43
|
FINISHED |
| Object | Gerry |
E175316
|
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: Gerry | Statement: [Gerardo, shortForm, Gerry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gerry Context triple: [Gerardo, shortForm, Gerry]
-
A.
Gerry
Gerry is a fictional character played by Irish actor Ciarán Hinds, known from his work in film and television dramas.
-
B.
Gerry
Gerry is a minimalist 2002 drama film directed by Gus Van Sant, starring Matt Damon and Casey Affleck as two friends lost in the desert.
-
C.
Gerry
Gerry is a surname most notably associated with Elbridge Gerry, an American statesman and fifth Vice President of the United States, whose name is the origin of the term "gerrymandering."
-
D.
Gerald
chosen
Gerald is a masculine given name of Germanic origin, commonly used in English-speaking countries.
-
E.
Gerald
Gerald is a central character in the British comedy film "The Full Monty," portrayed as a former factory foreman who joins a group of unemployed men planning a striptease act to make ends meet.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd43949e6c8190ae5e4fa119cde33a |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b06554748190bf76e3383413a3c4 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 9:54 p.m.