Triple
T790502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gregory of Nyssa |
E16902
|
entity |
| Predicate | givenName |
P17
|
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: [Gregory of Nyssa, givenName, Gregory]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gregory Context triple: [Gregory of Nyssa, givenName, 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.
Jeffrey
Jeffrey is a masculine given name of Germanic origin, commonly used in English-speaking countries.
-
C.
Andreas
Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
-
D.
Gavin
Gavin is a masculine given name of Celtic origin, commonly used in English-speaking countries.
-
E.
Anthony
Anthony is a masculine given name of Latin origin, commonly used in English-speaking countries.
- 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_69a4936cb7448190914f5fe4b8d81607 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a79754988190ab494b1c54d6a2a4 |
completed | March 1, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3afbb0c8190b4e7aa7824c8b787 |
completed | March 4, 2026, 3:14 a.m. |
Created at: March 1, 2026, 7:38 p.m.