Triple
T35703816
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farmington precinct |
E1031658
|
entity |
| Predicate | hasSetLocationRealWorld |
P81329
|
FINISHED |
| Object | Los Angeles, California |
—
|
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: Los Angeles, California | Statement: [Farmington precinct, hasSetLocationRealWorld, Los Angeles, California]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSetLocationRealWorld Context triple: [Farmington precinct, hasSetLocationRealWorld, Los Angeles, California]
-
A.
hasRealWorldOrigin
Indicates that something is derived from, based on, or directly connected to an actual entity, event, or source in the real world.
-
B.
portrayedByRealWorldLocation
Indicates that a fictional or represented location is depicted or substituted by an actual real-world location.
-
C.
exactLocationKnown
Indicates that the precise geographic or spatial position of an entity is known and specified.
-
D.
basedOnRealLocation
Indicates that something is derived from, inspired by, or modeled after an actual geographic place in the real world.
-
E.
setInFictionalOrRealLocation
chosen
Indicates that something (such as a story, event, or scene) takes place within a specified location, whether that location is real or fictional.
- 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_69f76e0d393c8190b6303c64408736db |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a002249ee388190a9501ee7630dc658 |
completed | May 10, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_6a002189273881909b6b687e2d61f5b1 |
completed | May 10, 2026, 6:11 a.m. |
Created at: May 3, 2026, 4:05 p.m.