Triple
T9864125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manila Ocean Park |
E239787
|
entity |
| Predicate | locatedOn |
P40
|
FINISHED |
| Object | Luneta |
E261775
|
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: Luneta | Statement: [Manila Ocean Park, locatedOn, Luneta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luneta Context triple: [Manila Ocean Park, locatedOn, Luneta]
-
A.
Luneta
chosen
Luneta is the historic urban park in Manila, Philippines, renowned as a national landmark and popular public gathering place.
-
B.
Lunay
Lunay is a Puerto Rican reggaeton and Latin trap singer known for hits like "Soltera" and collaborations with major urban Latin artists.
-
C.
Luna
Luna was an ancient Roman town in northern Italy that served as a key urban and commercial center for the Ligurian region.
-
D.
Luna
Luna is a Spanish surname most prominently associated with Mexican actor and filmmaker Diego Luna.
-
E.
Luna
Luna is the protagonist of the game "Lunar: The Silver Star," a classic Japanese role-playing game known for its character-driven story and fantasy adventure.
- 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_69ca84e7506c819095cbde4ff16512bb |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3b927d08190a45ff68de3954e8f |
completed | April 2, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e44dc0b8819082294a479299814e |
completed | April 5, 2026, 4:25 a.m. |
Created at: March 30, 2026, 8:36 p.m.