Triple
T31801653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FILL-uh-PEEN (radiotelephony pronunciation) |
E811755
|
entity |
| Predicate | refersToWord |
P63266
|
FINISHED |
| Object | Philippine |
—
|
NE NERFINISHED |
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 | Statement: [FILL-uh-PEEN (radiotelephony pronunciation), refersToWord, Philippine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToWord Context triple: [FILL-uh-PEEN (radiotelephony pronunciation), refersToWord, Philippine]
-
A.
refersSpecificallyTo
Indicates that one entity makes an explicit, precise reference to another particular entity, distinguishing it from more general or ambiguous references.
-
B.
alsoRefersTo
chosen
Indicates that one term, label, or identifier is used as an alternative designation for the same entity or concept as another.
-
C.
languageRefersTo
Indicates that a language is used to denote, describe, or refer to a particular entity, concept, or subject.
-
D.
oftenRefersTo
Indicates that one entity is frequently used to mention, denote, or reference another entity in common usage or context.
-
E.
refersToPerson
Indicates that one entity is making reference to, mentioning, or pointing specifically to a particular person.
- 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_69f348e70d188190b4637c5509f81274 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fd553d7cb881908d243e7a9f30ac85 |
completed | May 8, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69fd514dcb1c81908333c70d7edd79c9 |
completed | May 8, 2026, 2:58 a.m. |
Created at: April 30, 2026, 11:42 p.m.