Triple
T15338353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Harrelson |
E366725
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kenneth |
E561889
|
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: Kenneth | Statement: [Ken Harrelson, givenName, Kenneth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenneth Context triple: [Ken Harrelson, givenName, Kenneth]
-
A.
Kenneth
Kenneth is the full given name of American documentary filmmaker Ken Burns, renowned for his in-depth historical films and distinctive storytelling style.
-
B.
Kenneth
chosen
Kenneth is a masculine given name of Gaelic origin, commonly used in English-speaking countries.
-
C.
Kenneth
Kenneth is a central male character in the darkly comic stage play "The Woman Who Cooked Her Husband," whose infidelity and subsequent fate drive the plot’s themes of betrayal and revenge.
-
D.
Kenneth
Kenneth is the formal given name of American country music singer, songwriter, and actor Kenny Rogers.
-
E.
Jeffrey
Jeffrey is a masculine given name of Germanic 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e11b22c81908280efe65acd5454 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01f2ee9c819080fce24ed13a07c7 |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:17 a.m.