Triple
T4541977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manila American Cemetery |
E107553
|
entity |
| Predicate | hasReligiousStructure |
P916
|
FINISHED |
| Object | non-denominational chapel |
—
|
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: non-denominational chapel | Statement: [Manila American Cemetery, hasReligiousStructure, non-denominational chapel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReligiousStructure Context triple: [Manila American Cemetery, hasReligiousStructure, non-denominational chapel]
-
A.
hasReligiousOrganization
Indicates that an entity is associated with, governed by, or belongs to a specific religious organization.
-
B.
hasClericalStructure
Indicates that an entity possesses an organized clerical or administrative hierarchy or framework.
-
C.
hasReligiousInstitutionType
Indicates that an entity is associated with, or classified by, a specific type of religious institution.
-
D.
hasAssociatedReligion
Indicates that an entity is connected with or linked to a particular religion.
-
E.
hasReligiousSite
chosen
Indicates that a location or entity possesses, contains, or is associated with a religious site such as a temple, church, mosque, shrine, or similar place of worship.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57d219d88190a67ada845323d7fb |
completed | March 20, 2026, 2:21 p.m. |
| PD | Predicate disambiguation | batch_69bd5220e40481908ca2d7e2c43d8531 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:04 p.m.