Triple
T33628471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant William F. Kinderman |
E861477
|
entity |
| Predicate | characterAgeTrait |
P94896
|
FINISHED |
| Object | middle-aged |
—
|
LITERAL 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: middle-aged | Statement: [Lieutenant William F. Kinderman, characterAgeTrait, middle-aged]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterAgeTrait Context triple: [Lieutenant William F. Kinderman, characterAgeTrait, middle-aged]
-
A.
characterAgeDescriptor
chosen
Indicates how a character’s age is qualitatively described or categorized (e.g., young, middle-aged, elderly) rather than given as a specific number.
-
B.
agingCharacteristics
Indicates how the qualities, properties, or behavior of something change as it ages or over time.
-
C.
childCharacterTrait
Indicates that a child possesses or exhibits a particular character trait.
-
D.
associatedCharacterTrait
Indicates a relationship where a character is linked to, or described by, a particular trait or quality.
-
E.
AICharacteristics
Indicates the defining traits, behaviors, or properties that characterize an artificial intelligence system.
- F. None of above.
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_69f34981c54c81909b33c3fa2208a52d |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff48199b1c8190bb05872f8a4f4673 |
completed | May 9, 2026, 2:43 p.m. |
| PD | Predicate disambiguation | batch_69ff4746b1cc8190854f70a124df7d04 |
completed | May 9, 2026, 2:40 p.m. |
Created at: May 1, 2026, 1:41 a.m.