Triple
T35790485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phoenix Lights |
E1034679
|
entity |
| Predicate | neighboringStatesInvolved |
P23031
|
FINISHED |
| Object | Nevada |
—
|
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: Nevada | Statement: [Phoenix Lights, neighboringStatesInvolved, Nevada]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: neighboringStatesInvolved Context triple: [Phoenix Lights, neighboringStatesInvolved, Nevada]
-
A.
neighboringCountryInvolved
Indicates that a neighboring country is involved in or affected by a particular event, situation, or interaction.
-
B.
hasStateInvolved
Indicates that a particular state (as a political or administrative entity) participates in, is implicated in, or plays a role within a given situation, event, or relationship.
-
C.
borderingOpposingState
Indicates that one state shares a border with another state that is politically, militarily, or ideologically opposed to it.
-
D.
hasNeighbouringState
Indicates that one state shares a common border or is directly adjacent geographically to another state.
-
E.
affectedU.S.State
chosen
Indicates that a particular U.S. state is impacted or influenced by a specified event, condition, or entity.
- 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_69f76e1575908190aaa306d843b41c14 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b5ccbda481908fe1945c35e36ce8 |
completed | May 3, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c06f5881908f0b98cad6796478 |
completed | May 3, 2026, 8:49 p.m. |
Created at: May 3, 2026, 4:06 p.m.