Triple
T10806567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray M. Bowen |
E254981
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ray |
E19092
|
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: Ray | Statement: [Ray M. Bowen, givenName, Ray]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray Context triple: [Ray M. Bowen, givenName, Ray]
-
A.
Ray
chosen
Ray is a masculine given name commonly used in English-speaking countries, often as a short form of Raymond.
-
B.
Ray
"Ray" is a 2004 biographical film about the life and music of legendary rhythm and blues musician Ray Charles.
-
C.
Ray
Ray is an ancient city near modern-day Tehran in Iran that served as a significant political and cultural center in various Persian empires.
-
D.
Ray
Ray is a surname of English and Scottish origin borne by various notable individuals across different fields.
-
E.
Ray
Ray is an open-source distributed computing framework designed to scale Python applications for tasks like machine learning, reinforcement learning, and data processing across clusters.
- 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_69d733b3f92c8190bcc85db22d77bb7d |
completed | April 9, 2026, 5:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de5680566c8190bd4ce7a736dc0e46 |
completed | April 14, 2026, 3 p.m. |
Created at: April 8, 2026, 9:18 p.m.