Triple
T34192280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fa Mulan (singing voice) in Mulan |
E877140
|
entity |
| Predicate | characterGivenName |
P199880
|
FINISHED |
| Object | Mulan |
—
|
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: Mulan | Statement: [Fa Mulan (singing voice) in Mulan, characterGivenName, Mulan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterGivenName Context triple: [Fa Mulan (singing voice) in Mulan, characterGivenName, Mulan]
-
A.
characterName
Indicates that an entity has a specific name used to identify its character.
-
B.
characterSetName
Indicates the name assigned to a particular character set used for encoding or representing characters.
-
C.
character3
Indicates a tertiary or additional character role associated with an entity, typically the third distinct character linked within a given context or work.
-
D.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
E.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
- 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_69f349af20a4819089ac24d28f2d8112 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff6074bcd4819090b72cd6209ff206 |
completed | May 9, 2026, 4:27 p.m. |
| PD | Predicate disambiguation | batch_69ff600aba888190812a6e7eca0283b8 |
completed | May 9, 2026, 4:25 p.m. |
| PDg | Predicate description generation | batch_69ff607407a881909d9e6e1c98559a6e |
completed | May 9, 2026, 4:27 p.m. |
Created at: May 1, 2026, 1:55 a.m.