Triple
T11810478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atakapa |
E280857
|
entity |
| Predicate | peopleStatus |
P42493
|
FINISHED |
| Object | culturally surviving descendants |
—
|
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: culturally surviving descendants | Statement: [Atakapa, peopleStatus, culturally surviving descendants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peopleStatus Context triple: [Atakapa, peopleStatus, culturally surviving descendants]
-
A.
peopleCountDescriptor
Indicates how the number of people involved in a situation, group, or context is characterized or described.
-
B.
peopleGroupWithin
Indicates that one group of people is geographically or organizationally contained within another group of people.
-
C.
peopleType
chosen
Indicates the classification or category of people an entity is associated with or represents.
-
D.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
E.
hasCrowdLevel
Indicates the degree or intensity of how crowded a place, event, or situation is.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a658f918819092c2db05fe2ab0ce |
completed | April 10, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69d8a24e9a088190aff7932d1ff93dbf |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:42 p.m.