Triple
T16048519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andy Hopper |
E389287
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Andy |
E818092
|
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: Andy | Statement: [Andy Hopper, givenName, Andy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andy Context triple: [Andy Hopper, givenName, Andy]
-
A.
Andy
Andy is the central protagonist of the British film "Life Is Sweet," around whom the story’s domestic and emotional themes revolve.
-
B.
Andy
Andy is a fictional character portrayed by actor Ryan Hansen, known for his comedic and often charmingly awkward roles.
-
C.
Andy
Andy is the central character in the 1991 Australian psychological drama film "Proof," around whom the story’s exploration of trust, perception, and relationships revolves.
-
D.
Andy
chosen
Andy is a common English given name, often used as a diminutive of Andrew.
-
E.
Andy
Andy is the immortal warrior leader portrayed by Charlize Theron in the action-fantasy film "The Old Guard."
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18360464881909fd4d3bcb4ffb7f5 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbddc25481908fca660c4f14eaff |
completed | May 10, 2026, 1:14 a.m. |
Created at: April 10, 2026, 4:56 a.m.