Triple
T15522177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieb–Liniger model |
E368994
|
entity |
| Predicate | hasParticleStatistics |
P119002
|
FINISHED |
| Object | bosonic |
—
|
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: bosonic | Statement: [Lieb–Liniger model, hasParticleStatistics, bosonic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParticleStatistics Context triple: [Lieb–Liniger model, hasParticleStatistics, bosonic]
-
A.
hasStatistics
Indicates that an entity is associated with one or more statistical measures, records, or summaries describing its quantitative properties or performance.
-
B.
hasStatisticsPage
Indicates that an entity is associated with a dedicated page or resource presenting statistical information about it.
-
C.
hasParticleType
Indicates that an entity is associated with, composed of, or characterized by a specific type or category of particle.
-
D.
hasPassengerUsageStatistics
Indicates the relationship by which an entity is associated with data describing how passengers use it, such as counts, frequencies, or patterns of passenger activity.
-
E.
partOfStatisticalSystem
Indicates that one entity functions as a component or subsystem within a broader statistical system or framework.
- F. None of above. chosen
Provenance (4 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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0403543188190abac49d2b9decb89 |
completed | April 16, 2026, 1:49 a.m. |
| PD | Predicate disambiguation | batch_69ded28ab0588190a47a9090d1238707 |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded57165288190979b7acb71ad5145 |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 4:04 a.m.