Triple
T11611532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippine Reclamation Authority |
E275394
|
entity |
| Predicate | typeOfProjectHandled |
P37446
|
FINISHED |
| Object | urban land reclamation |
—
|
LITERAL 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: urban land reclamation | Statement: [Philippine Reclamation Authority, typeOfProjectHandled, urban land reclamation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfProjectHandled Context triple: [Philippine Reclamation Authority, typeOfProjectHandled, urban land reclamation]
-
A.
typeOfProject
Indicates the specific category or kind of project that an entity is associated with or classified under.
-
B.
typicalProjectTypes
chosen
Indicates the kinds or categories of projects that are most commonly or characteristically associated with a given entity.
-
C.
typeOfCasesHandled
Indicates the categories or kinds of cases that an entity (such as a person, organization, or system) is responsible for managing or processing.
-
D.
eligibleProjectType
Indicates that a project belongs to a category or type that qualifies it for a specific program, process, or benefit.
-
E.
subjectOfProject
Indicates that an entity serves as the main focus, topic, or target of a particular project.
- F. None of above.
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_69d6aaf84b548190ac072e4fb89ae18f |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a043a3c08190a20cbc2ba5a8d218 |
completed | April 10, 2026, 7:01 a.m. |
| PD | Predicate disambiguation | batch_69d85dd6503c819081f9045e9d5c4f3f |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.