Triple
T15956328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margarita Island |
E386943
|
entity |
| Predicate | hasResortArea |
P10436
|
FINISHED |
| Object | Porlamar |
E1185901
|
NE 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: Porlamar | Statement: [Margarita Island, hasResortArea, Porlamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Porlamar Context triple: [Margarita Island, hasResortArea, Porlamar]
-
A.
Porlamar
chosen
Porlamar is the main commercial and tourist city on Margarita Island in northeastern Venezuela, known for its beaches, shopping, and port activities.
-
B.
Kolona
Kolona is an important archaeological site on the Greek island of Aegina, featuring remains from prehistoric through classical periods, including fortifications and temple structures.
-
C.
Aibonito
Aibonito is a mountainous municipality in central Puerto Rico known for its cool climate and flower festival.
-
D.
Abataranika
Abataranika is a Bengali literary work that served as the source material for the film "Mahanagar."
-
E.
San-Pédro
San-Pédro is a major port city in southwestern Côte d'Ivoire, known especially for its role in the export of cocoa and other goods.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156fb29848190a55cabb49cb19575 |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf16b1b881909768d18b889260da |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:53 a.m.