Triple
T15097423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naomi Groves |
E360574
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Groves |
E73431
|
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: Groves | Statement: [Naomi Groves, familyName, Groves]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Groves Context triple: [Naomi Groves, familyName, Groves]
-
A.
Groves
chosen
Groves is a surname most notably associated with U.S. Army General Leslie R. Groves Jr., who directed the Manhattan Project during World War II.
-
B.
Monrow
Monrow is an alternative spelling of the name Monroe, which is commonly used as both a surname and given name and is associated with various notable people and places.
-
C.
Firgrove
Firgrove is a suburban area forming part of the town of Milnrow in Greater Manchester, England.
-
D.
Eagle Rock
Eagle Rock is a prominent sandstone outcrop and popular hiking destination within the Santa Monica Mountains of Southern California.
-
E.
Sharswood
Sharswood is a residential neighborhood in North Philadelphia known for its ongoing redevelopment and proximity to areas like Brewerytown and Girard College.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005466e9c8190a68e1fbeb8922b1a |
completed | April 15, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feb7e47b20819084145008474f47b7 |
completed | May 9, 2026, 4:28 a.m. |
Created at: April 10, 2026, 3:04 a.m.