Triple
T119407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Third World |
E2411
|
entity |
| Predicate | originOfTerm |
P453
|
FINISHED |
| Object | analogy with Third Estate in pre-revolutionary France |
—
|
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: analogy with Third Estate in pre-revolutionary France | Statement: [Third World, originOfTerm, analogy with Third Estate in pre-revolutionary France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originOfTerm Context triple: [Third World, originOfTerm, analogy with Third Estate in pre-revolutionary France]
-
A.
etymologyType
Indicates the specific kind or category of etymological relationship that links a term to its linguistic origin or source.
-
B.
etymology
chosen
Indicates the historical origin and development of a word or term, including its source language and form.
-
C.
etymologicalLanguage
Indicates the language from which a word or term is historically derived in its etymology.
-
D.
coinedTerm
Indicates that an entity originated and introduced a particular term or expression into use.
-
E.
historicalOrigin
Indicates the relationship by which one entity serves as the source, origin, or starting point in history for another entity.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a258e0b11c8190b7b5cf3c354c47ce |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a25646d5088190a057989c32da3a90 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.