Triple
T14200267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of Djibouti |
E351944
|
entity |
| Predicate | daggersMeaning |
P79329
|
FINISHED |
| Object | traditional weapons of local peoples |
—
|
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: traditional weapons of local peoples | Statement: [Coat of arms of Djibouti, daggersMeaning, traditional weapons of local peoples]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: daggersMeaning Context triple: [Coat of arms of Djibouti, daggersMeaning, traditional weapons of local peoples]
-
A.
degMeaning
Indicates that one entity specifies or encodes the meaning or semantic interpretation of another entity.
-
B.
ermenMeaning
Indicates that one entity represents or conveys the meaning or semantic interpretation of another entity.
-
C.
duMeaning
chosen
Indicates that one entity expresses, conveys, or signifies a particular meaning or sense in relation to another.
-
D.
handMeaning
Indicates that one entity uses or positions its hand in a particular way to convey a specific meaning, message, or communicative intent toward another entity.
-
E.
doabaMeaning
Indicates that an entity has the specific meaning or semantic interpretation associated with the term "doaba."
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61f472548190a1a7edc40526eac3 |
completed | April 14, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69de05bcd7d48190a4848d9320404aa6 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:04 a.m.