Triple
T19442955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phila II |
E486395
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Phila |
—
|
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: Phila | Statement: [Phila II, givenName, Phila]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Phila Context triple: [Phila II, givenName, Phila]
-
A.
Phila
chosen
Phila was a prominent Macedonian noblewoman and political figure of the early Hellenistic period, known for her influential role in the power struggles following Alexander the Great’s death.
-
B.
Filadelfia
Filadelfia is a small municipality and town located in the Caldas Department of Colombia, known for its coffee-growing economy in the Andean region.
-
C.
Filadelfia
Filadelfia is a town in the Pando Department of northern Bolivia, located in the Amazon rainforest region near the border with Brazil.
-
D.
Filadelfia
Filadelfia is a small town in the Calabria region of southern Italy, known for its historic center and traditional southern Italian culture.
-
E.
Philadelphia
Philadelphia is a 1993 American legal drama film that broke ground in mainstream cinema for its portrayal of HIV/AIDS, homophobia, and discrimination in the workplace.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63386c520819092bea5d7f259a226 |
completed | April 20, 2026, 2:09 p.m. |
Created at: April 10, 2026, 1:38 p.m.