Triple
T14722952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francis Dolarhyde |
E345860
|
entity |
| Predicate | teethCharacteristic |
P35359
|
FINISHED |
| Object | uses dentures with filed teeth |
—
|
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: uses dentures with filed teeth | Statement: [Francis Dolarhyde, teethCharacteristic, uses dentures with filed teeth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teethCharacteristic Context triple: [Francis Dolarhyde, teethCharacteristic, uses dentures with filed teeth]
-
A.
distinguishingDentalFeature
Indicates that one entity has a dental characteristic that serves to differentiate it from another entity or group.
-
B.
hasTeeth
Indicates that one entity possesses teeth as a physical feature.
-
C.
dentition
chosen
Indicates the type, arrangement, or condition of teeth that an entity possesses.
-
D.
incisorCount
Indicates the number of incisor teeth an entity has.
-
E.
toothUsedBy
Indicates that a tooth is utilized or employed by a particular entity (such as an organism or tool) for some function or action.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25e9a14819081fa06fc601f295d |
completed | April 14, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69de657e174481909da0437556334a04 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.