Triple
T4122603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lapu-Lapu City |
E92647
|
entity |
| Predicate | hasComponentBarangays |
P54409
|
FINISHED |
| Object | urban barangays |
—
|
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 barangays | Statement: [Lapu-Lapu City, hasComponentBarangays, urban barangays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasComponentBarangays Context triple: [Lapu-Lapu City, hasComponentBarangays, urban barangays]
-
A.
hasNumberOfBarangays
Indicates the total count of barangays associated with a given administrative unit or locality.
-
B.
barangay
Indicates that an entity is associated with, located in, or falls under the jurisdiction of a specific barangay (the smallest local administrative division).
-
C.
hasTownship
Indicates that one administrative area or jurisdiction includes or is associated with a specific township.
-
D.
hasComponentCity
Indicates that an entity includes or is composed of one or more cities as its constituent parts.
-
E.
hasComarca
Indicates that one entity is associated with, or belongs to, a specific comarca (an administrative or territorial district).
- F. None of above. chosen
Provenance (4 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_69aed9685f70819086932777aec8d959 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69af0246e40081908ad6741a830ca68e |
completed | March 9, 2026, 5:24 p.m. |
| PD | Predicate disambiguation | batch_69af01867698819098e4144634b2ec4f |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af0245adbc81908b89a40850047975 |
completed | March 9, 2026, 5:24 p.m. |
Created at: March 9, 2026, 3:41 p.m.