Triple
T20692872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ulos Bintang Maratur |
E508593
|
entity |
| Predicate | carriesMeaningFor |
P41499
|
FINISHED |
| Object | family relationships |
—
|
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: family relationships | Statement: [Ulos Bintang Maratur, carriesMeaningFor, family relationships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carriesMeaningFor Context triple: [Ulos Bintang Maratur, carriesMeaningFor, family relationships]
-
A.
hasParticularSignificanceFor
chosen
Indicates that something holds a special, notable, or contextually important relevance or impact for a particular entity or situation.
-
B.
hasMeaningCategory
Indicates that something is associated with a particular category of meaning or semantic type.
-
C.
hasLiteralMeaning
Indicates that one entity expresses the direct, explicit meaning or sense of another entity (such as a word, phrase, or symbol).
-
D.
hasMeaningInChinese
Indicates that one entity (such as a word, phrase, or symbol) possesses a specific meaning or interpretation within the Chinese language.
-
E.
hasMultipleMeanings
Indicates that a term, symbol, or expression is associated with more than one distinct meaning or interpretation.
- 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c10fc4088190ab71ef078600954b |
completed | April 21, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69e5c044d1108190b2b5d25de23f6401 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:09 p.m.