Triple
T2962090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tigrinya Muslims |
E80071
|
entity |
| Predicate | sharesCuisineWith |
P45241
|
FINISHED |
| Object | other Tigrinya speakers |
—
|
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: other Tigrinya speakers | Statement: [Tigrinya Muslims, sharesCuisineWith, other Tigrinya speakers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesCuisineWith Context triple: [Tigrinya Muslims, sharesCuisineWith, other Tigrinya speakers]
-
A.
sharesElementsWith
Indicates that two entities have one or more elements or components in common.
-
B.
similarDish
Indicates that two dishes share notable similarities, such as ingredients, preparation methods, flavor profile, or style.
-
C.
sharesTraditionsWith
Indicates that two entities have customs, practices, or cultural traditions in common or mutually observe similar traditional activities.
-
D.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
-
E.
cuisineFeature
Indicates a characteristic, quality, or notable aspect that describes or distinguishes a particular cuisine.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9955e6488190bea170724d5fbfe8 |
completed | March 8, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69ad960c5c8881909d679912bd7d78f3 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad98379fac8190a4dfe530787703c9 |
completed | March 8, 2026, 3:39 p.m. |
Created at: March 8, 2026, 2:57 p.m.