Triple
T36512145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manuel Antonio National Park |
E899935
|
entity |
| Predicate | hasEntranceTown |
P195374
|
FINISHED |
| Object | Manuel Antonio |
—
|
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: Manuel Antonio | Statement: [Manuel Antonio National Park, hasEntranceTown, Manuel Antonio]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEntranceTown Context triple: [Manuel Antonio National Park, hasEntranceTown, Manuel Antonio]
-
A.
hasEntrance
Indicates that one entity possesses or provides an entry point or access way to another entity or space.
-
B.
hasEntrancesIn
Indicates that an entity has one or more entrances located within or opening into another specified entity or area.
-
C.
hasEntranceOn
Indicates that one entity’s entrance or access point is located on or faces a specified side, boundary, or feature of another entity.
-
D.
hasAccessToTown
Indicates that one entity is permitted or able to enter, use, or otherwise access a particular town.
-
E.
hasLandmarkAtEntrance
Indicates that a specific landmark is located at or directly adjacent to the entrance of something.
- F. None of above. chosen
Provenance (4 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_69f76e5dada881909da2d34bc7a9202a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fdbc5ef46c8190bbcfb9798f4615b7 |
completed | May 8, 2026, 10:35 a.m. |
| PD | Predicate disambiguation | batch_69fdbb270338819082ce3f73903e884f |
completed | May 8, 2026, 10:29 a.m. |
| PDg | Predicate description generation | batch_69fdbc5da9988190b95234bce4cc2062 |
completed | May 8, 2026, 10:35 a.m. |
Created at: May 3, 2026, 4:10 p.m.