Triple
T18815835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | beryllium |
E460131
|
entity |
| Predicate | neutronAbsorptionCrossSection |
P19732
|
FINISHED |
| Object | low |
—
|
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: low | Statement: [beryllium, neutronAbsorptionCrossSection, low]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: neutronAbsorptionCrossSection Context triple: [beryllium, neutronAbsorptionCrossSection, low]
-
A.
thermalNeutronCaptureCrossSection
chosen
Indicates the probability that a nucleus will capture a thermal (low-energy) neutron, expressed as an effective interaction cross-sectional area.
-
B.
neutronEmissionRate
Indicates the rate at which neutrons are emitted from a source or system over a given period of time.
-
C.
interactionCrossSection
Indicates the effective likelihood or probability that a specified interaction or reaction will occur between entities (such as particles) under given conditions.
-
D.
neutronSpectrum
Indicates the energy distribution or range of neutrons present in a given system, interaction, or environment.
-
E.
usesNeutronModerator
Indicates that one entity employs another entity as a neutron moderator to slow down neutrons in a nuclear process or 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a3e092e081908fc310c70f646e79 |
completed | April 20, 2026, 3:56 a.m. |
| PD | Predicate disambiguation | batch_69e48d1b10ec8190985c6fb5766ff981 |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:53 a.m.