Triple
T30430443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tracy Strauss |
E774147
|
entity |
| Predicate | canFreeze |
P19069
|
FINISHED |
| Object | water |
—
|
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: water | Statement: [Tracy Strauss, canFreeze, water]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canFreeze Context triple: [Tracy Strauss, canFreeze, water]
-
A.
neverFreezes
Indicates that the subject is never in a state of freezing, i.e., it does not reach or experience freezing conditions under any circumstances.
-
B.
freezesOver
chosen
Indicates that a liquid surface becomes solid due to low temperatures, typically forming a layer of ice over it.
-
C.
usesWeightFreezing
Indicates that an entity applies weight freezing, keeping certain model parameters fixed and untrainable during learning or optimization.
-
D.
reasonForNotFreezing
Indicates the explanation or cause for why a particular thing, process, or condition is not frozen or has not been subjected to freezing.
-
E.
frozenIn
Indicates that one entity is immobilized or preserved in a solid, frozen state within or by another entity.
- 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_69f22492d2a88190995ce8745d9becaa |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6866c549c8190985ef4bf8032209e |
completed | May 2, 2026, 11:19 p.m. |
| PD | Predicate disambiguation | batch_69f678d2196c8190b9d0d2fcd47cc539 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 8:06 p.m.