Triple
T7028117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maren Morris |
E163199
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object | Grey |
E488411
|
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: Grey | Statement: [Maren Morris, associatedAct, Grey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grey Context triple: [Maren Morris, associatedAct, Grey]
-
A.
Grey
chosen
Grey is a common English surname borne by numerous notable figures across entertainment, politics, and history.
-
B.
Gray
Gray is the commonly used short form of the name Gray Davis, the former governor of California.
-
C.
Gray
Gray is a historic commune in eastern France known for its picturesque setting along the Saône River and its well-preserved old town.
-
D.
Gray
Gray is a common English surname of Anglo-Saxon origin, often associated with families from Britain and Ireland.
-
E.
Brown
Brown is a common English-language surname of Anglo-Saxon origin, typically derived from a nickname referring to hair color, complexion, or clothing.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1fee32081908eff988b18daa6d0 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7758c87908190bd1ddcfdccc171b5 |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:35 p.m.