Triple
T37915920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sedona, Arizona |
E945816
|
entity |
| Predicate | hasEarlySettler |
P56858
|
FINISHED |
| Object | Theodore Carlton Schnebly |
—
|
NE NERFINISHED |
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: Theodore Carlton Schnebly | Statement: [Sedona, Arizona, hasEarlySettler, Theodore Carlton Schnebly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEarlySettler Context triple: [Sedona, Arizona, hasEarlySettler, Theodore Carlton Schnebly]
-
A.
hasNotableEarlySettler
chosen
Indicates that an entity is associated with a historically significant person who was among its earliest settlers.
-
B.
settlerOf
Indicates that an entity established or inhabited a place as a settler of that location.
-
C.
hasHumanSettlement
Indicates that a location or area contains or is the site of a human settlement, such as a town, village, or city.
-
D.
numberOfInitialSettlers
Indicates the quantity of settlers present at the initial establishment of a settlement or colony.
-
E.
firstPermanentSettler
Indicates that the subject is the earliest individual or group to establish a lasting, continuous residence in the location or entity specified by the object.
- 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_69f76ef2ebd88190be5229f2621070b3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbc7b78f9481909f4f8fc2e3fdcde1 |
completed | May 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69fbbd18c9908190928d274f8731dfa8 |
completed | May 6, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:20 p.m.