Triple
T5164967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerry Dipoto |
E116528
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gerard |
E150605
|
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: Gerard | Statement: [Jerry Dipoto, givenName, Gerard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gerard Context triple: [Jerry Dipoto, givenName, Gerard]
-
A.
Gerard
chosen
Gerard is a masculine given name of Germanic origin, commonly used in various European countries.
-
B.
Gabriele
Gabriele is a feminine given name of Italian origin, commonly used in various European countries.
-
C.
Thaddaeus
Thaddaeus is a disciple of Jesus traditionally counted among the Twelve Apostles in Christian tradition, often identified with Jude the Apostle.
-
D.
Gerald
Gerald is a masculine given name of Germanic origin, commonly used in English-speaking countries.
-
E.
Gerald
Gerald is the birth name of Jerry Brown, the longtime Democratic politician and former governor of California.
- 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_69bd445edb3881909b93b34d260717fc |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd792936d48190825da8826448c1da |
completed | March 20, 2026, 4:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed937bd8c81909569f7205044aa5a |
completed | March 21, 2026, 5:45 p.m. |
Created at: March 20, 2026, 1:44 p.m.