Triple
T18370590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bajorans |
E446170
|
entity |
| Predicate | cardassianTerm |
P24083
|
FINISHED |
| Object | Bajora |
—
|
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: Bajora | Statement: [Bajorans, cardassianTerm, Bajora]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cardassianTerm Context triple: [Bajorans, cardassianTerm, Bajora]
-
A.
classificationTerm
Indicates that one entity serves as a categorical label or type used to classify or group another entity.
-
B.
keyTerm
Indicates that a term functions as a primary or central concept within a given context or information structure.
-
C.
languageTerm
chosen
Indicates that one entity is a linguistic expression (word, phrase, or term) used to denote or label the other entity.
-
D.
typicalTerm
Indicates that something is a standard, representative, or characteristic term typically associated with a given concept or context.
-
E.
coordinateTerm
Indicates that two terms are coordinate concepts at the same hierarchical level, typically sharing a common broader category without one being subordinate to the other.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e5175324e48190a00572e15423feb7 |
completed | April 19, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69e44fed3fdc81908f4ed6a81db42416 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:44 a.m.