Triple
T29277290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camp Crame |
E742276
|
entity |
| Predicate | occupant |
P75
|
FINISHED |
| Object |
PNP Legal Service
PNP Legal Service is the legal affairs office of the Philippine National Police, providing legal assistance, advice, and representation for the organization and its personnel.
|
E1859823
|
NE 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: PNP Legal Service | Statement: [Camp Crame, occupant, PNP Legal Service]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: PNP Legal Service Triple: [Camp Crame, occupant, PNP Legal Service]
Generated description
PNP Legal Service is the legal affairs office of the Philippine National Police, providing legal assistance, advice, and representation for the organization and its personnel.
Provenance (5 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_69f0912124d48190a046642b69407f4c |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f665130c7081908d42c2d803ed47d1 |
completed | May 2, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a25893d748c81909f427642cfa4f2cd |
completed | June 7, 2026, 3:07 p.m. |
| NEDg | Description generation | batch_6a258e5fdc0c8190ac32db7404b5ef6c |
completed | June 7, 2026, 3:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2593a40fa48190b705582f4be60140 |
completed | June 7, 2026, 3:52 p.m. |
Created at: April 28, 2026, 12:51 p.m.