Triple
T12724537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ifugao languages |
E304068
|
entity |
| Predicate | arealTypology |
P106583
|
FINISHED |
| Object | Philippine-type languages |
—
|
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: Philippine-type languages | Statement: [Ifugao languages, arealTypology, Philippine-type languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: arealTypology Context triple: [Ifugao languages, arealTypology, Philippine-type languages]
-
A.
arealFeature
Indicates a relationship where something is characterized as a spatial or geographic feature occupying an area on a surface or map.
-
B.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
C.
urbanDistrictType
Indicates the classification of an urban district according to its specific type or category within an administrative or planning system.
-
D.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
E.
urbanDesignType
Indicates the specific category or style of urban design that characterizes or is applied to a place or project.
- 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96403957c81909acdee7bdae71696 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d87078c819083ea724238992204 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:25 p.m.