Triple
T29898740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Eko |
E759349
|
entity |
| Predicate | encountersEntity |
P84701
|
FINISHED |
| Object | Smoke Monster |
—
|
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: Smoke Monster | Statement: [Mr. Eko, encountersEntity, Smoke Monster]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encountersEntity Context triple: [Mr. Eko, encountersEntity, Smoke Monster]
-
A.
hasEncounter
Indicates that one entity experiences or comes into contact with another entity or event, typically in a specific context or situation.
-
B.
encountersCharacter
chosen
Indicates that one character comes into contact with or meets another character, typically within a particular situation or context.
-
C.
notableEncounterWith
Indicates that an entity has had a significant or noteworthy meeting, interaction, or confrontation with another entity.
-
D.
encountersDoctor
Indicates that one entity comes into contact with or meets a doctor in some context or situation.
-
E.
encounterFrequency
Indicates how often two entities come into contact or interact with each other over a given period.
- 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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6772d534c8190be386d44e7f60a4e |
completed | May 2, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:05 p.m.