Triple
T32948575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Marikina |
E842876
|
entity |
| Predicate | specializedIndustry |
P176031
|
FINISHED |
| Object | shoe production |
—
|
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: shoe production | Statement: [City of Marikina, specializedIndustry, shoe production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: specializedIndustry Context triple: [City of Marikina, specializedIndustry, shoe production]
-
A.
economicSpeciality
chosen
Indicates a relationship where an entity is characterized by, or primarily engaged in, a particular economic field, sector, or type of economic activity.
-
B.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
C.
marketSpecialization
Indicates a relationship where an entity focuses its activities, products, or services on serving a specific segment or niche of a broader market.
-
D.
industrialFocus
Indicates a relationship where an entity is primarily concerned with, specialized in, or directed toward a particular industrial sector or area of industrial activity.
-
E.
supportedIndustry
Indicates that one entity provides backing, resources, or services to help sustain or advance a particular industry.
- 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_69f3494a31f481909057136e49b4fe60 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f7b5ccbda481908fe1945c35e36ce8 |
completed | May 3, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c06f5881908f0b98cad6796478 |
completed | May 3, 2026, 8:49 p.m. |
Created at: May 1, 2026, 1:21 a.m.