Triple
T8932551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flag of Liberia |
E212691
|
entity |
| Predicate | blueFieldRepresents |
P38846
|
FINISHED |
| Object | African mainland |
—
|
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: African mainland | Statement: [Flag of Liberia, blueFieldRepresents, African mainland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: blueFieldRepresents Context triple: [Flag of Liberia, blueFieldRepresents, African mainland]
-
A.
blueRepresents
chosen
Indicates that the color blue is used to symbolize, denote, or stand for a particular concept, state, or category in a given context.
-
B.
fieldRepresents
Indicates that one field or attribute stands for, encodes, or symbolizes another concept, value, or entity.
-
C.
field3Represents
Indicates that the third field in a data structure or record serves as a representation or encoding of some specific value, concept, or attribute.
-
D.
field4Represents
Indicates that the fourth field in a structured record encodes or stands for a particular value, concept, or attribute rather than containing a literal or direct value.
-
E.
blueShade
Indicates that one entity has a color that is a specific shade or variation of blue.
- 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_69ca8395c438819087d7cb844ab5990c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc668e5c108190b08f9cd6b4fd4a8b |
completed | April 1, 2026, 12:27 a.m. |
| PD | Predicate disambiguation | batch_69cc5ed3286c8190a21de2ee11f2639f |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:57 p.m.