Triple
T28335955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florencio Ávalos |
E717670
|
entity |
| Predicate | cityNearAccident |
P125033
|
FINISHED |
| Object | Copiapó |
—
|
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: Copiapó | Statement: [Florencio Ávalos, cityNearAccident, Copiapó]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityNearAccident Context triple: [Florencio Ávalos, cityNearAccident, Copiapó]
-
A.
nearbyCurrent
Indicates that one entity is located close to another entity at the present moment or in the current context.
-
B.
nearbyEvent
Indicates that one event occurs close in space or time to another event.
-
C.
nearestCityTo
Indicates that one city is the closest in distance to a given location or entity compared to all other cities.
-
D.
infrastructureNearby
Indicates that one entity is located close to another entity that serves as infrastructure (such as roads, utilities, or public facilities).
-
E.
nearbyCrashConflictContext
chosen
Indicates a contextual relationship where a crash event occurs in close spatial or temporal proximity to another relevant element, creating a potential conflict or interaction between them.
- 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_69eff6e9a57c8190a69c2c74b5d72119 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64bd515048190a935a0a579b55299 |
completed | May 2, 2026, 7:09 p.m. |
| PD | Predicate disambiguation | batch_69f641e2f1708190b45b48d6a43c51d2 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 28, 2026, 12:36 a.m.