Triple

T36182986

Position	Surface form	Disambiguated ID	Type / Status
Subject	Roar	`E1046764`	entity
Predicate	notableCastInjury	`P58567`	FINISHED
Object	Melanie Griffith suffered facial injuries from a lion attack Melanie Griffith suffered facial injuries from a lion attack while working with untrained big cats during the production of the 1981 film "Roar."	`E2173390`	NE FINISHED

How this triple was built (3 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: Melanie Griffith suffered facial injuries from a lion attack | Statement: [Roar, notableCastInjury, Melanie Griffith suffered facial injuries from a lion attack]

NEDg Description generation gpt-5.1

Instruction

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Input

Entity: Melanie Griffith suffered facial injuries from a lion attack
Triple: [Roar, notableCastInjury, Melanie Griffith suffered facial injuries from a lion attack]

Generated description

Melanie Griffith suffered facial injuries from a lion attack while working with untrained big cats during the production of the 1981 film "Roar."

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: notableCastInjury
Context triple: [Roar, notableCastInjury, Melanie Griffith suffered facial injuries from a lion attack]

A. notableInjuryCount
Indicates the number of significant injuries associated with an entity, event, or period.
B. injuredIn chosen
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
C. hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
D. majorInjuryTrack
Indicates that an entity has sustained a significant or severe injury that is being recorded or monitored over time.
E. injuryType
Indicates the specific kind or category of injury associated with an entity or event.
F. None of above.

Provenance (6 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_69f76e3c1b10819081fc7a807a71cf84`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_6a037c8d06cc8190ab6a5e18d9d2571e`	completed	May 12, 2026, 7:16 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a39340d888c8190b062fb762bfee921`	completed	June 22, 2026, 1:09 p.m.
NEDg	Description generation	`batch_6a393551ea208190a075eb301aa99bc7`	completed	June 22, 2026, 1:14 p.m.
NED2	Entity disambiguation (via description)	`batch_6a3935ed3c3c8190bf17fe2eb6eb45d4`	completed	June 22, 2026, 1:17 p.m.
PD	Predicate disambiguation	`batch_6a037a0a54cc8190868c1bfa1590d1a6`	completed	May 12, 2026, 7:05 p.m.

Created at: May 3, 2026, 4:08 p.m.