Triple
T17147119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lapu-Lapu City |
E416118
|
entity |
| Predicate | hasFormerName |
P65
|
FINISHED |
| Object | Opon |
E416118
|
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: Opon | Statement: [Lapu-Lapu City, hasFormerName, Opon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Opon Context triple: [Lapu-Lapu City, hasFormerName, Opon]
-
A.
Opon
chosen
Opon is the former name of what is now Lapu-Lapu City, a highly urbanized city located on Mactan Island in the Philippines.
-
B.
Opañel
Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
-
C.
Ōpunake
Ōpunake is a small coastal town on the west coast of New Zealand’s North Island, known for its surf beach and views of Mount Taranaki.
-
D.
Opo
The Opo are a small Nilotic ethnic group living primarily in the borderlands of western Ethiopia and South Sudan, known for their agro-pastoralist lifestyle and distinct language and culture.
-
E.
Opol
Opol is a coastal municipality in Misamis Oriental, Philippines, known for its beaches, eco-tourism attractions, and proximity to Cagayan de Oro City.
- 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_69d886d15af4819092f92f8a129763e6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f2db20d48190b5d69ccf89f3bc42 |
completed | April 18, 2026, 9:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a014158ed8c8190adb3c03c8a114a59 |
completed | May 11, 2026, 2:39 a.m. |
Created at: April 10, 2026, 5:36 a.m.