Triple
T1539072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lemon test |
E32822
|
entity |
| Predicate | hasProng |
P29634
|
FINISHED |
| Object | secular purpose prong |
—
|
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: secular purpose prong | Statement: [Lemon test, hasProng, secular purpose prong]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProng Context triple: [Lemon test, hasProng, secular purpose prong]
-
A.
hasRingComposition
Indicates that one entity is composed of, or structurally organized around, a ring-like arrangement or circular structure.
-
B.
hasLuster
Indicates that one entity possesses a shiny, glossy, or reflective surface quality.
-
C.
hasClasps
Indicates that one entity is equipped with or features clasps that fasten, secure, or attach it to another entity or its parts.
-
D.
hasHorns
Indicates that an entity possesses horns as a physical feature.
-
E.
associatedMetal
Indicates a relationship where one entity is linked or connected to a particular metal, such as by composition, usage, origin, or symbolic association.
- 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_69a885ed29088190a3c2d5a3d100c16e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a915f323bc8190aa757142c225e0ae |
completed | March 5, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69a907b046448190be8ea4d7b20255f7 |
completed | March 5, 2026, 4:33 a.m. |
| PDg | Predicate description generation | batch_69a915f1694081908f87b509eda1309f |
completed | March 5, 2026, 5:34 a.m. |
Created at: March 4, 2026, 7:26 p.m.