Triple
T14148939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tagish |
E350623
|
entity |
| Predicate | hasEndangermentLevel |
P60801
|
FINISHED |
| Object | severely endangered |
—
|
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: severely endangered | Statement: [Tagish, hasEndangermentLevel, severely endangered]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEndangermentLevel Context triple: [Tagish, hasEndangermentLevel, severely endangered]
-
A.
hasHazardLevel
chosen
Indicates that an entity is associated with a specified degree or category of risk or danger.
-
B.
isEndangeredDueTo
Indicates that an entity is endangered as a result of the specific cause or factor represented by the related entity.
-
C.
hasEmergencyLevel
Indicates that an entity is associated with a specific degree or severity of emergency status.
-
D.
hasConservationConcernIn
Indicates that an entity is subject to a specified conservation concern status within a particular geographic area or jurisdiction.
-
E.
hasEndemismLevel
Indicates the degree to which something is restricted or native to a particular geographic area or region.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61237ef481909374c1f68a2370b7 |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b8434c81908c33b1b513463b12 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 12:55 a.m.