Triple
T19988216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Baumann |
E493989
|
entity |
| Predicate | hasNotableBook |
P127003
|
FINISHED |
| Object | Say, Cut, Map |
—
|
NE NERFINISHED |
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: Say, Cut, Map | Statement: [Ken Baumann, hasNotableBook, Say, Cut, Map]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Say, Cut, Map Context triple: [Ken Baumann, hasNotableBook, Say, Cut, Map]
-
A.
Say, Cut, Map
chosen
"Say, Cut, Map" is a novel by American writer Ken Baumann, known for its experimental, fragmented narrative and exploration of memory, identity, and trauma.
-
B.
Speaking in Strings
Speaking in Strings is a documentary film that explores the life, artistry, and emotional intensity of virtuoso violinist Nadia Salerno-Sonnenberg.
-
C.
Get Out the Map
"Get Out the Map" is a folk-rock song by the Indigo Girls, known for its introspective lyrics and themes of travel, self-discovery, and emotional journeying.
-
D.
String Interlude
String Interlude is a short musical transition piece featured on the album "Love the Future" by Chester French.
-
E.
CUT
CUT is a public university in Limassol, Cyprus, known for its focus on applied research and technology-oriented academic programs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fddfee081909aa8cd6e279b2a7a |
completed | April 20, 2026, 5:18 p.m. |
Created at: April 11, 2026, 3:30 p.m.