Triple
T36958301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heavy Weapon Dude |
E914242
|
entity |
| Predicate | teamRoleInEncounters |
P186807
|
FINISHED |
| Object | support fire unit |
—
|
LITERAL 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: support fire unit | Statement: [Heavy Weapon Dude, teamRoleInEncounters, support fire unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teamRoleInEncounters Context triple: [Heavy Weapon Dude, teamRoleInEncounters, support fire unit]
-
A.
roleInAppointments
Indicates the specific function or capacity an entity holds within one or more scheduled appointments.
-
B.
hasPatientRole
Indicates that an entity participates in a relationship or activity specifically in the role of a patient (the one receiving care, treatment, or action).
-
C.
clinicalRole
Indicates the specific function, responsibility, or position an entity holds within a clinical or healthcare context.
-
D.
relationshipWithDoctorParnassus
Indicates that an entity has a personal or professional relationship with Doctor Parnassus.
-
E.
hasEncounter
Indicates that one entity experiences or comes into contact with another entity or event, typically in a specific context or situation.
- 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_69f76e8c498c8190b2842db80aea8b3b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fa0a7b00948190a257273d9968c5d7 |
completed | May 5, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69f9fec9c9488190ae2a349651a02782 |
completed | May 5, 2026, 2:29 p.m. |
| PDg | Predicate description generation | batch_69fa0a799b9081909bfa8293a22c4b00 |
completed | May 5, 2026, 3:19 p.m. |
Created at: May 3, 2026, 4:13 p.m.