Triple
T7696013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rizal |
E174371
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Laguna |
E190593
|
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: Laguna | Statement: [Rizal, borders, Laguna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laguna Context triple: [Rizal, borders, Laguna]
-
A.
Laguna
chosen
Laguna is a province in the Philippines known for its hot springs, lakeside towns around Laguna de Bay, and as the birthplace of national hero José Rizal.
-
B.
Laguna
Laguna is the internal codename Apple used for its early Macintosh Portable computer model.
-
C.
Lagunas
Lagunas is a municipality and town in the state of Jalisco, Mexico, known for its rural character and proximity to the Sierra de Amula region.
-
D.
Laguna San Rafael
Laguna San Rafael is a glacial lagoon in southern Chile famed for its dramatic icebergs and proximity to the San Rafael Glacier within Laguna San Rafael National Park.
-
E.
Laguna Miscanti
Laguna Miscanti is a high-altitude Andean lake in northern Chile famed for its deep blue waters, surrounding volcanoes, and striking desert landscape.
- 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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70267dab88190ac8e3f643343bf13 |
completed | March 27, 2026, 10:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8acaa6004819088f1ae45ad9b378e |
completed | March 29, 2026, 4:38 a.m. |
Created at: March 27, 2026, 4:03 p.m.