Triple
T5043193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Twin Pawns |
E113594
|
entity |
| Predicate | hasTwinSistersTheme |
P60981
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [The Twin Pawns, hasTwinSistersTheme, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTwinSistersTheme Context triple: [The Twin Pawns, hasTwinSistersTheme, yes]
-
A.
hasTwin
Indicates that one entity is a twin of another, sharing the same birth event or time with a sibling.
-
B.
hasTwinFeature
Indicates that two entities share an identical or nearly identical feature, characteristic, or component, as if they are twins in that respect.
-
C.
hasSister
Indicates that one entity is the sister of another entity.
-
D.
twinType
Indicates that one entity is classified as a specific type or category of twin in relation to another entity.
-
E.
hasSisterChair
Indicates that one chair is related to another chair as its sister, typically implying a closely associated or counterpart chair within the same set or context.
- 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_69bd44391fc48190a311ce9c826c209b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73fc04f08190aba851fa0192d0fb |
completed | March 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69bd71529d608190a53470ba6c14bb1d |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd73617f348190b2fa68a0ef4fc7b1 |
completed | March 20, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:37 p.m.