Triple
T10798856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grzegorz |
E254784
|
entity |
| Predicate | equivalentNameInEnglish |
P3437
|
FINISHED |
| Object | Gregory |
E50625
|
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: Gregory | Statement: [Grzegorz, equivalentNameInEnglish, Gregory]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gregory Context triple: [Grzegorz, equivalentNameInEnglish, Gregory]
-
A.
Gregory
chosen
Gregory is a masculine given name of Greek origin, historically associated with figures such as the American actor Gregory Peck.
-
B.
Gregory
Gregory is an electoral district in Queensland, Australia, known for its vast rural area and strong agricultural and mining industries.
-
C.
Leodore
Leodore is the first name of Mayor Lionheart, the lion politician who serves as the mayor of Zootopia in Disney's animated film "Zootopia."
-
D.
Gerard
Gerard is a masculine given name of Germanic origin, commonly used in various European countries.
-
E.
Grigor
Grigor is a given name, commonly used in various Eastern European and Caucasian cultures, that corresponds to the English name Gregory.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73334feb08190aae967eaa37659f7 |
completed | April 9, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a9033148819095c6e7485c3a73e6 |
completed | April 18, 2026, 3:53 p.m. |
Created at: April 8, 2026, 9:17 p.m.