Triple
T3423007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moneda Street, Santiago |
E72155
|
entity |
| Predicate | hasNearbySecurityPresence |
P22957
|
FINISHED |
| Object | presidential guard |
—
|
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: presidential guard | Statement: [Moneda Street, Santiago, hasNearbySecurityPresence, presidential guard]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbySecurityPresence Context triple: [Moneda Street, Santiago, hasNearbySecurityPresence, presidential guard]
-
A.
hasNotableNearbyEntity
Indicates that one entity has another significant or noteworthy entity located in its close physical or contextual proximity.
-
B.
hasSecurityPresence
chosen
Indicates that some form of security personnel, system, or measures are present at or associated with an entity or location.
-
C.
hasNearbySanctuary
Indicates that one entity has a sanctuary or place of refuge located close to it in space or distance.
-
D.
hasNearbySquare
Indicates that one entity has at least one square-shaped entity located close to it in space.
-
E.
hasNearbyMode
Indicates that one entity has another entity located close enough to be considered in its immediate vicinity or surrounding area.
- 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_69ad85ad38e48190b7660c5118a35289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb95223e081908b2954769d2f46c8 |
completed | March 8, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69adadfea024819094b41a13bc004bda |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:15 p.m.