Triple
T18435628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makati, Michigan (historical relationship) |
E450382
|
entity |
| Predicate | relationshipScope |
P131589
|
FINISHED |
| Object | municipal |
—
|
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: municipal | Statement: [Makati, Michigan (historical relationship), relationshipScope, municipal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipScope Context triple: [Makati, Michigan (historical relationship), relationshipScope, municipal]
-
A.
relationshipContext
Indicates the situational or social setting in which a relationship between entities exists or occurs.
-
B.
addressesRelationship
Indicates that one entity directs communication, remarks, or attention specifically toward another entity.
-
C.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
relationshipFocus
Indicates a relationship where particular attention, priority, or emphasis is placed on the connection between two or more entities.
-
E.
relationshipNetwork
Indicates a connection between entities that are linked through a web of relationships, often capturing how they are associated or interact within a broader network.
- 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_69d8d381d6388190a9e94e9c658174e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e51c0bd35c8190b66d62ad9987377f |
completed | April 19, 2026, 6:16 p.m. |
| PD | Predicate disambiguation | batch_69e469c943a4819094c8fdc5971ad3a7 |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2aa72c8190a40854a7a52081e2 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:28 a.m.