Triple
T14636311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Water |
E343615
|
entity |
| Predicate | thermalConductivityAt25°C |
P19721
|
FINISHED |
| Object | about 0.6 W/(m·K) |
—
|
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: about 0.6 W/(m·K) | Statement: [Water, thermalConductivityAt25°C, about 0.6 W/(m·K)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thermalConductivityAt25°C Context triple: [Water, thermalConductivityAt25°C, about 0.6 W/(m·K)]
-
A.
thermalConductivity
chosen
Indicates how effectively heat is conducted through a material per unit temperature gradient.
-
B.
thermalConductivityRank
Indicates the relative ordering of entities based on how effectively they conduct heat.
-
C.
thermalExpansionCoefficient
Indicates how much a material's size changes per unit length (or volume) for each degree change in temperature.
-
D.
thermalProperty
Indicates a relationship where an entity is associated with a characteristic describing its behavior or response with respect to temperature or heat.
-
E.
electricalConductivity
Indicates that one entity has the ability to conduct electric current through it, typically quantified as a measure of how easily charge flows within that material or medium.
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4ab9578819085b4cf7244d30d87 |
completed | April 14, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69de657359c88190b082e3e9f86fc1d7 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:26 a.m.