Triple
T8143359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Divided Line |
E190148
|
entity |
| Predicate | lowestCognitiveState |
P80917
|
FINISHED |
| Object | eikasia |
—
|
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: eikasia | Statement: [Divided Line, lowestCognitiveState, eikasia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lowestCognitiveState Context triple: [Divided Line, lowestCognitiveState, eikasia]
-
A.
lowestCategory
Indicates that an entity belongs to the most specific or least general category within a classification hierarchy.
-
B.
lowestRank
Indicates that the subject has the least or worst rank in an ordered set compared to all other related entities.
-
C.
lowestGrade
Indicates that one entity has the smallest or worst grade value compared to all other relevant entities in a given context.
-
D.
lowestWorld
Indicates that the referenced world has the minimal value (e.g., in rank, cost, or some ordering) among a set of worlds.
-
E.
lowestScore
Indicates that the associated value is the smallest (minimum) score among a set of scores.
- 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_69ca82bd9900819099477cdc2eb4244f |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4444bb248190beaaa2ce4b8f3eaa |
completed | March 31, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69cb369c0d0481908762c488d7f77e74 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb39d20e78819092ea9e04357be008 |
completed | March 31, 2026, 3:04 a.m. |
Created at: March 30, 2026, 5:36 p.m.