Triple
T18435194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ayala Center |
E450371
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Greenbelt 1 |
—
|
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: Greenbelt 1 | Statement: [Ayala Center, hasPart, Greenbelt 1]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greenbelt 1 Context triple: [Ayala Center, hasPart, Greenbelt 1]
-
A.
Greenbelt Town
Greenbelt Town is a planned community in Greenbelt, Maryland, originally developed as a New Deal-era federal housing project.
-
B.
Greenbelt Park
Greenbelt Park is a wooded national park in Greenbelt, Maryland, offering camping, hiking, and other outdoor recreation just outside Washington, D.C.
-
C.
Greenbelt Park
Greenbelt Park is a public recreational green space located in Upland, California.
-
D.
Udny Green
Udny Green is a small rural village in Aberdeenshire, Scotland, known for its traditional village green and historic parish church.
-
E.
Greenbelt
chosen
Greenbelt is a prominent upscale shopping, dining, and lifestyle complex located in the central business district of Makati in Metro Manila, Philippines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d381d6388190a9e94e9c658174e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e51c0bd35c8190b66d62ad9987377f |
completed | April 19, 2026, 6:16 p.m. |
Created at: April 10, 2026, 11:28 a.m.